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Architectural Engineering -- Faculty Publications Architectural Engineering

4-2017

How Tonality and Loudness of Noise relate toAnnoyance and Task Performancerdquo Noise ControlEng J 65(2) 71-82Joonhee LeeUniversity of Nebraska - Lincoln gi20plusgmailcom

Jennifer M FrancisUniversity of Nebraska - Lincoln jmfrancis624gmailcom

Lily M WangUniversity of Nebraska - Lincoln lwang4unledu

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Lee Joonhee Francis Jennifer M and Wang Lily M How Tonality and Loudness of Noise relate to Annoyance and TaskPerformancerdquo Noise Control Eng J 65(2) 71-82 (2017) Architectural Engineering -- Faculty Publications 103httpdigitalcommonsunleduarchengfacpub103

How tonality and loudness of noise relate to annoyanceand task performance

Joonhee Leea) Jennifer M Francisb) and Lily M Wangb)

(Received 4 September 2015 Revised 9 November 2016 Accepted 7 February 2017)

Audible tones in noise generated by building mechanical equipment can be a leadingcause of complaints from occupants A number of metrics have been developed toquantify prominence of a tone but previous work has shown that the impact of a cer-tain tonality appears to vary with the level of the broadband noise signal More workon how tonal signals of varying tonality tone frequency and broadband noise levels re-late to annoyance and task performance is needed This paper investigates such rela-tionships between current noise metrics annoyance and task performance underassorted tonal noise conditions through subjective testing Participants rated their per-ceived annoyance after being exposed to noise signals with differing levels of toneswhile solving Sudoku puzzles In addition to assessing annoyance the test also sur-veyed the perceived workload caused by the noise by using a modified noise-inducedtask load index questionnaire Five levels of tonal prominence for each of two tonal fre-quencies were added above two different ambient background noise levels to create20 noise signals of interest The task performance results based on the Sudoku puz-zle answers show trends of decreasing accuracy with increasing tone strengths butthe differences are not statistically significant Other findings are that loudnessmetrics are most highly correlatedwith annoyance responses while tonalitymetricsdemonstrate relatively less but also significant correlation with annoyance Gener-ally participants felt more annoyedwith higher background noise levels lower tonefrequency andmore prominent tone strength Based on correlation analysis a mul-tiple regressionmodel using two of themost strongly correlated noisemetrics ANSIloudness level and tonal audibility has been developed for predicting annoyanceresponses from tonal noise conditionscopy2017 Institute of Noise Control Engineering

Primary subject classification 632 Secondary subject classification 131

1 INTRODUCTION

Most mechanical systems in buildings generate signif-icant tones due to rotating components HVAC (heatingventilating and air conditioning) equipment in buildingsare becoming more energy-efficient but these changesare often accompanied with changing sound quality in-cluding more prominent tones Increasing the tonalityof the noise though can result in increased complaintsfrom building occupants and neighbors but quantitative

data published to date are not able to establish evidence-based guidelines or limits for tones in different levels ofbuilding equipment noise Noise regulations in manymunicipalities in the United States apply a 5 dB penaltyif tones are detected using a one-third octave band mea-surement technique given in ISO 1996-22007 AnnexD1 when comparing against maximum allowed noiselevels2ndash5 However the one-third octave band measure-ment technique is not always capable of detecting a tonalcomponent if the tone falls on the edge of two bandsThe 5 dBA penalty value is also rather arbitrary as thatvalue has not been determined from psychoacoustic stud-ies the same 5 dB penalty is applied once a tone is deemedto be prominent but more prominent tones are not penal-ized more greatly than less prominent ones

A considerable amount of literature has been publishedon the relationship between tones in noise and human an-noyance as perceptible tones in noise from aircraft officeequipment and wind turbines have been recognized as

a) Charles W Durham School of Architectural Engineeringand Construction University of Nebraska mdash LincolnOmaha Nebraska 68182 USA Department of BuildingCivil and Environment Engineering Concordia UniversityQuebec Montreal H3G 2W1 CANADA email joonheeleeconcordiaca

b) Charles W Durham School of Architectural Engineeringand Construction University of Nebraska mdash LincolnOmaha Nebraska 68182 USA

71Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

serious sources of public noise pollution since the 1960sIn the 1980s Hellman found that tonal components inbroadband spectra impact ratings of annoyance loudnessand noisiness6 and that the number of tones and frequencydifferences between tones as well as the frequency of thetone itself influence annoyance7 More and Davies8 alsoexamined the effects of tones on human annoyance fromaircraft flyover noise using a questionnaire without anyaccompanying tasks They found that regression modelsthat include metrics for both loudness and tonality matchedwell with annoyance responses from their time-varyingsignals Lee et al9 investigated the tonality perceptionfor harmonic complex tones and pointed out the diffi-culties of quantifying overall tonality including harmonictones with existing methods Hastings et al10 investigatedassorted tonality metrics for predicting tonality and an-noyance of noises They proposed modifications incalculating the existing metrics and suggested that thebandwidth and roll-off rate of tones should be includedfor accurate tonality perception for aircraft noise

More recent attention has focused on perception oftones in noise from building machinery Ryherd andWang11 investigated assorted building mechanical noisesamples and showed that current indoor noise criteria werenot accurately reflecting annoyance because the criteria donot typically account for tonal characteristics in assess-ment Susini et al12 used multidimensional scaling analy-sis to find that one of the most important sound qualitydimensions of noise from indoor air-conditioning unitsis the ratio of tonal harmonic components to broadbandnoise components Berglund et al13 also investigated per-ception of environmental noises including ventilation-likenoise spectra with the multidimensional scaling methodol-ogy and concluded that spectral contrast which is relatedto the tonality is the best acoustic index for predicting thepreference rating of noises Besides laboratory studiesLandstroumlm et al14 explored noise levels and annoyanceby occupants in actual working spaces They found thatthe relation between noise levels and annoyance wasweak but annoyance ratings were significantly increasedwhen tones were present in the noise These previousstudies strongly suggest that tonality metrics should be in-cluded when evaluating noise from mechanical systems inbuildings but to date none of existing tonal metrics is uti-lized broadly and there is still limited understanding inlinking measurable objective metrics to annoyance

Besides annoyance how tones in noise impact taskperformance is also of interest Previous research find-ings into effects of tones on human performance havebeen inconsistent and limited Landstroumlm et al1415 foundthat task performance was significantly lower for tonalnoises and Laird16 argued that tones above 512 Hz havea greater effect on increasing error rates of tasks in arti-ficial factory experiments Grjmaldi17 also found tendencies

of slower response times and increasing error rates of coor-dinated movement performance for tones in the rangeof 2400 to 4800 Hz However a few other studies1118

did not find any statistically significant differences intask performance between broadband and tonal noises

This paper describes a subjective investigation on howexposure to tonal noise as produced by building mech-anical systems impacts human annoyance and task per-formance using a larger variety of signals than mostprevious studies The relationships between a number ofknown noise metrics objectively describing both loud-ness and tonality and annoyance responses are examinedResults are also used to develop a preliminary annoyanceprediction model through statistical analysis based on anoise signals loudness and tonalityWhile harmonic struc-tures of tones have been shown to impact annoyance andother psychoacoustic qualities such as sharpness rough-ness or fluctuation may play a part as well those aspectswere not directly considered in this investigation Ratherthis study focused on how these two primary characteris-tics of loudness and tonality affect annoyance and perfor-mance because previous studies pointed out that thetonality impulsivity and loudness have the most influen-tial impacts on listeners responses

There is a degree of uncertainty in defining annoyancedue to noise ISOTS 156662003 defines noise-inducedannoyance as ldquoone persons individual adverse reaction tonoise in various ways including dissatisfaction bother an-noyance and disturbancerdquo19 While a variety of definitionsfor annoyance have been suggested it is generally agreedthat annoyance is concerned with physical noise character-istics the context of measurement and personal attributesof listeners20 In this study the physical noise characteris-tics of interest are loudness and tonality Although thesubjective testing has been conducted in a controlled lab-oratory the context of the measurement is meant to be likean office environment From reviewing previous researchstudies Marquis-Favre et al21 indicated that among non-acoustic factors that can influence annoyance fear andnoise sensitivity were found to have the most significanteffects In the investigation discussed herein fear was notconsidered since listeners are not expected to fear regularlevels of building mechanical noise but noise sensitivitywas surveyed as a personal attribute

The noise metrics investigated in this paper that havebeen developed to quantify tonality or the degree to whichtones are present in broadband noise are reviewed ANSIS1210-2010Part1 Annex D22 presents tone-to-noise ratio(TNR) and prominence ratio (PR) to quantify tonality andISO1996-22007AnnexC5 suggests tonal audibility (ΔLta)These metrics are calculated from the steady-state fre-quency spectrum of the noise recording through digitalfast Fourier transform analysis There are two main dif-ferences between tonal audibility and the previous two

72 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

metrics tone-to-noise ratio and prominence ratio Onemajor difference is that tonal audibility uses A-weightedsound pressure levels and includes a frequency correctionterm in its calculation so that the prominence criteria oftones is constant across frequencies whereas TNR andPR ratings are based on unweighted sound pressure levelsConsequently the prominence of tones is frequency de-pendent for TNR and PR ratings but not for ΔLta Thatis PR = 5 for a 100Hz tone is not necessarily the same per-ceived tonality as PR = 5 for a 500 Hz tone The other dif-ference is that the tonal audibility uses a linear regressionline instead of actual noise components when calculatingmasking tonal levels within the critical bands The equa-tion to calculate ΔLta is given by

ΔLta frac14 Lpt Lpn thorn 2 dBthorn log 1thorn fc502

25

eth1THORN

where Lpt is the total sound pressure level of the tones Lpnis the total sound pressure level of the masking noise in thecritical band and fc is the center frequency of the criticalband Based on the tonal audibility calculation penaltyfactors between 0 and 6 dB are provided to adjust the over-all A-weighted noise levels rather than setting prominencecriteria It also requires separate analysis for each tonewithin a multi-tonal noise signal Aures tonality (Aures)is another metric for tonality that considers the frequencyas well as bandwidth and levels of all tonal componentsthrough use of weighting functions23 It is one of the fewthat can account for multiple tones in a signal

Popular loudness metrics are also investigated in thisstudy because previous studies have found that loudnessof the noise is the most relevant feature correlating to an-noyance besides tonality Among the included loudnessmetrics are A-weighted (dBA) and unweighted (dB)equivalent sound pressure levels and stationary loudnesslevels calculated according to ANSI S34-200724 (ANSIloudness) and ISO 532-1975 B method25 (ISO loudness)The ISO loudness and ANSI loudness are based onZwickers26 and Glasberg and Moores27 loudness mod-els respectively They both can use stationary one-thirdoctave band data for the calculation of loudness

A few noise metrics that consider both loudness andtonality to produce an overall rating for tonal noiseshave been proposed These combined metrics basicallyadd penalty values to the loudness levels due to thepresence of tones The Joint Nordic Method (JNM) isstandardized in ISO 1996-220071 where the penaltyk values are derived from tonal audibility and addedto A-weighted sound pressure level Perceived noiselevel (PNL) was implemented to quantify subjective an-noyance of aircraft noise calculated from one-third oc-tave band values tone-corrected perceived noise level

(PNLT) is a revised version of PNL with the additionof a tone correction factor28 Sound quality indicator (SQI)is a similar metric suggested by the Air-ConditioningHeating and Refrigeration Institute to rate the sound qual-ity of building mechanical product noise based on one-third octave bands29 but it has yet to be applied widely

2 METHODOLOGY

21 Test Laboratory

The subjective testing was completed in an acoustictesting chamber at the University of Nebraska Figure 1illustrates a schematic plan of the testing chamber whichhas a volume of approximately 278 m3 The chamber isacoustically isolated from a monitor room and nearbyspaces Materials in the room include carpeted floorgypsum board walls with additional absorptive panelsacoustic bass traps and acoustical ceiling tiles The av-erage mid-frequency reverberation time is 031 secondsand the ambient background noise level is 37 dBAwhenair-conditioning in the chamber is turned off Figure 2presents the ambient background noise levels in thechamber across octave bands The tonal test signals weregenerated through a ceiling-mounted Armstrong i-ceilingspeaker and a sub-woofer in a corner The i-ceiling speakerappears as other ceiling tiles in the ceiling grid so thatparticipants cannot visually identify the location of thesound source Participants sat in the middle of the cham-ber and were advised not to move their location duringthe experiment

22 Test Signals

A total of 22 noise signals were generated for use inthis study by the program Test Tone Generator fromEsser Audio Two levels of broadband noise without any

Fig 1mdashSchematic plan of the acoustic testingchamber at the University of Nebraska

73Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

tonal components were used either 40 or 55 dBA overallfollowing a 5 dBoctave room criteria (RC) contour30These two levels are in the range of common backgroundnoise levels found in buildings A single tone at one oftwo frequencies and at one of five prominence levelswas added separately to the broadband noise signalsto create the other 20 noise signals The two tonal fre-quencies were selected to be 125 Hz which is a com-mon tone generated by building mechanical equipmentand 500 Hz as it is slightly higher but still in the fre-quency range where a number of other building mechan-ical equipment exhibit tones The five tone levels wereselected to range from below to above the prominencethresholds listed in ANSI S1210-201022 PR = 18 dBfor 125 Hz and PR = 12 dB for 500 Hz Table 1 pre-sents the prominence ratio values for each test signalFigure 3 illustrates the one-third octave band spectra ofthe test signals All tonal signals were measured using a BampK 4189-A microphone through the BampK PULSE sys-

tem at the listeners ear position in the testing chamberand averaged over a minute for calculation of noise metricsThe metrics were calculated using Matlab or programs pro-vided by the associated standards

23 Test Participants and Procedure

Ten participants four females and six males wererecruited from the University of Nebraska mdash Omahacommunity ranging in age from 25 to 43 years oldThe University of Nebraska mdash Lincoln InstitutionalReview Board approved the study and each participantwas paid for their time The sample size was determinedby a priori power analysis using the effect size from Moreand Davies8 statistical results using GPower version3131 The effect size for multiple regression modelsCohens f 2 was calculated as 669 from the squared mul-tiple correlation values in the previous study The minimum

Fig 2mdashMeasured octave band spectra for theambient background noise in the testchamber when air-conditioning is off

Table 1mdashProminence ratios for the tones in the noisestimuli used in the subjective testing aslisted by tonal frequency broadband back-ground noise level and tone level

Frequency(Hz)

BNL(dBA)

Prominence ratio (dB)

Tonelevel1

Tonelevel2

Tonelevel3

Tonelevel4

Tonelevel5

125 40 15 18 21 24 2755 13 15 18 21 24

500 40 9 12 15 18 2155 6 9 12 15 18

Fig 3mdashMeasured one-third octave bandspectra for a few of the test noisesignals (a) Broadband 40 dBA signaland some with assorted tones(b) Broadband 55 dBA signal andsome with assorted tones Tones wereeither at 125 or 500 Hz for clarity onlythe lowest and highest tonal strengthsare presented

74 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

sample size was then found to be six participants to achieve80 power (1 b) at two-sided 5 significance level (a)Based on this finding and available research funds a test-ing plan was designed to assess 22 signals across tentest subjects

All participants completed an orientation session includ-ing a hearing screening test before participation and dem-onstrated normal hearing with thresholds below 25 dBhearing level (HL) from 125 Hz to 8 kHz The noisesensitivity of each participant was also measured by areduced version (13 items only) of the Noise-Sensitivity-Questionnaire (NoiSeQ) by Schutte et al32 during the ori-entation session The participants were asked to answereach item using a four-level rating scale (strongly agree = 1slightly agree = 2 slightly disagree = 3 strongly dis-agree = 4) The responses were averaged across all itemsto form a composite scale to quantify the noise sensitivityfor each participant

The main test consisted of two parts a direct assess-ment with task (part A) and a magnitude adjustment test(part B) The results of part B have been presented inanother paper33 and hence are not included herein In partA participants were asked to complete as many Sudokunumber puzzles as possible while exposed to a broad-band noise signal some with assorted tonal compo-nents for 10 minutes Sudoku puzzles were selected asthe measure of task performance as they are compact toadminister easy to explain to test participants and havebeen used as a measure of task performance in otherstudies with results showing significant relationship withworking memory3435 All participants practiced solvingSudoku puzzles during the orientation session before par-ticipating in the main test and the difficulty of all Sudokupuzzles in the main test was held constant The puzzleswere all nine by nine with forty of the eighty-one grids be-ing prefilled with numbers

After spending 10 minutes solving the Sudoku puz-zles the subjects answered five questions on a subjec-tive questionnaire about the noise they had just heardThe questionnaire was a modified version of the NASAtask load index36 The original NASA task load index isdivided into six subscales mental demand physical de-mand temporal demand performance effort and frus-tration In this study the questions on physical demandtemporal demand and frustration were not included in-stead questions were added on rating loudness and annoy-ance incurred by noise as shown in Table 2 Participantsresponded to each question based on a 21-point scale (tomatch the scale from the original NASA task load index)on a paper form

Part A consisted of ten 30-minute sessions that werecompleted by each subject individually on differentdays Within each 30-minute session subjects were ex-posed to three noise signals (each for 10 minutes) and

thus completed three sequences of Sudoku puzzles (dif-ferent puzzles each time) followed by the questionnaireTo minimize the influence of back-to-back comparisonsof tonal noise conditions a neutral background noisecondition without any tonal components was used asthe second signal within each 30-minute test sessionWithin a single 30-minute test session the noise levelof the broadband noise without consideration for anytonal components remained at a constant level either40 or 55 dBA The presentation order of the backgroundnoise levels and tonal test signals was carefully balancedacross all subjects using a Latin square design

Two task performance measures were gathered by(1) counting the amount of Sudoku puzzles a subjectcompleted within a 10-minute trial with partial comple-tions included as well and (2) quantifying the accuracyof the puzzle answers in terms of correct numbers amongthose answered in a puzzle The maximum and mini-mum number of Sudoku puzzles participants completedin one 10-minute session were 28 and 03 and the max-imum and minimum accuracy of the puzzles were 100and 69

3 RESULTS AND DISCUSSION

The reliability of each participants responses wasdetermined from correlation analysis of the participantsindividual annoyance responses to a loudness metrictonality metric and average ratings across participants8Figure 4 presents correlation coefficients of each parti-cipants annoyance responses to the ANSI loudnesslevel tonal audibility and mean values across partici-pants Two participants responses (number 6 and 8)were excluded from all analyses because they ratedresponses randomly regardless of sound characteristics(correlation value lt02) The subject-to-loudness co-efficient of participant 6 was 017 and the subject-to-

Table 2mdashItems from the subjective questionnaireas modified from the NASA task loadindex

Description Questions

Mental demand 1 How mentally demandingwas the task

Overall performance 2 How successful wereyou in accomplishing whatyou were asked to do

Effort 3 How hard did you haveto work to accomplish yourlevel of performance

Loudness 4 How loud was the noiseAnnoyance 5 How annoying was the noise

75Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

tonality correlation coefficient of participant 8 was 003All subsequent analyses are based on the remaining eightsubjects which is still above the minimum sample size ofsix recommended from the a priori power analysis Addi-tionally outlier responses of annoyance and task perfor-mance scores were excluded from the statistical analysispresented below The outliers were identified using the cri-terion of being beyond three standard deviations from theaverage across participants Two outliers of annoyance rat-ings and three outliers of task performance scores wereexcluded for analyses based on this criterion

31 Relating Task Performance to SubjectiveResponses and to Noise Attributes

The task performance measures related to the Sudokupuzzles were correlated to the participants subjective

responses on the modified NASA task load index ques-tionnaires (Table 3) Spearmans correlation (r) was utilizedbecause not all of the variables met the assumption ofhaving a normal distribution with the sample size uti-lized An additional ldquoTLX-avgrdquo score was calculated asthe averaged value of all five items from the modified sur-vey to represent an overall rating of subjective task loadperception induced by noise exposure Since the task diffi-culty was held constant with equivalently difficult Sudokupuzzles throughout the experiment the variations in sub-jective ratings observed within subjects can be consideredas the result of varying background noise conditions Jobet al37 have recommended against using a single questionitem about annoyance because of its reduced validity con-sequently the composite modified Noise TLX rating isproposed as an alternative in this laboratory study Witha Cronbachs a coefficient for the reliability of 082 anda testndashretest correlation of the Noise TLX measure forthe stability of 077 the ldquoTLX-avgrdquo questionnaire wasfound to be internally consistent and stable over timeand thus suitable for the purpose of this test

As Table 3 indicates most of the subjective responseswere significantly correlated with each other Specificallyof interest the mental demand responses showed highcorrelations with perceptions of loudness and annoy-ance of the noise and as expected loudness and annoy-ance ratings were significantly correlated with each other(r = 0948) The only statistically significant correla-tion between a task performance result and a subjec-tive response was between ldquoaccuracyrdquo (accuracy ratesof participants puzzle answers) and responses to theldquoperformancerdquo question on the questionnaire (r =0483)

Figures 5 and 6 present the averaged task perfor-mance of the accuracy and number of completed puzzles

Fig 4mdashCorrelation coefficients of eachparticipants annoyance responses toeach signals ANSI loudness tonalaudibility and group average

Table 3mdashSpearmans correlation analysis of the subjective responses and Sudoku puzzle task performancemeasures TLX-avg is the average value of the responses to all five questions on the modified taskload index questionnaire ldquoNo of completedrdquo refers to the number of completed puzzles for eachtrial and ldquoaccuracyrdquo indicates accuracy rates of participants puzzle answers

Mentaldemand

Performance Effort Loudness Annoyance TLX-avg

No ofcompleted

Accuracy

Mental demand ndashPerformance 0260 ndashEffort 0610 0496 ndashLoudness 0501 0105 0230 ndashAnnoyance 0528 0162 0398 0948 ndashTLXndashavg 0631 0374 0601 0880 0956 ndashNo of puzzles completed 0317 0438 0394 0074 0020 0171 ndashAccuracy 0105 0483 0071 0289 0252 0330 0080 ndash

Correlation is significant at the 001 level (2-tailed)Correlation is significant at the 005 level (2-tailed)

76 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

against the physical attributes of the noise signals in-cluding background noise level tone frequency andthe five levels of tone strengths The repeated measureANOVA (analysis of variance) confirms that there wereno statistically significant differences between task per-formances across the various noise attributes Thussubjects did not complete more puzzles or have higheraccuracy under any particular tonal frequency back-ground noise level or tone strength although there

appears to be a slight tendency of lower accuracy withgreater tone strength

32 Relating Noise Attributes toAnnoyance Responses

To understand how the physical aspects of the noisesignals (background noise level tone frequency andtonal strength) related to annoyance a three-way repeated

Fig 5mdashAveraged accuracy of Sudoku puzzle answers as task performance scores plotted against(a) Background noise level (b) Tonal frequency and (c) Strength of the tones where Tone1 indicates the least prominent tone and Tone 5 indicates the most prominent tone Errorbars indicate one standard error

Fig 6mdashAveraged number of completed Sudoku puzzles as task performance scores plotted against(a) Background noise level (b) Tonal frequency and (c) Strength of the tones where Tone1 indicates the least prominent tone and Tone 5 indicates the most prominent tone Errorbars indicate one standard error

77Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

measure ANOVAwas conducted Mauchlys test indicatedthat the assumption of sphericity had been met for themain effects of tonal strength and its interactions withtone frequency and background noise level The analysisshows a significant main effect of background noise level[F(17) = 8261 p lt 0001 2p = 092] tone frequency

[F(17) = 2001 p = 0003 2p = 074] and tonal strength

[F(428) = 476 p = 0005 2p = 041] on annoyanceThe main analysis shows that the 55 dBA based

tonal signals were significantly more annoying than40 dBA based tonal signals and that the 125 Hz tonalsignals were significantly more annoying than 500 Hztonal signals Contrast comparisons reveal that the 4thhighest [F(17) = 10420 p = 0014] and 5th highest[F(17) = 12069 p = 0010] in prominence tonal signalswere perceived as more annoying than the least (1st)prominent tonal signals

Figure 7 illustrates the mean annoyance ratings acrossbackground noise levels tonal frequencies and tonestrengths Summarizing these results the overall back-ground noise level does impact annoyance with higherlevels leading to greater annoyance The lower fre-quency tone generated greater annoyance ratings butone should note that the prominence levels of the125 Hz tone versus those of the 500 Hz tone used inthe study were not the same even though the relativedifferences from the threshold of tones presented inISO 1996-22007 are the same There was also a sig-nificant interaction effect between background noiselevel and tone frequency [F(17) = 3331 p = 0014

2p = 060] As plotted in Fig 7(d) the difference be-tween annoyance ratings of the 125 and 500 Hz toneswas greater with the 40 dBA background noise levelcondition than with the 55 dBA background noiselevel condition It appears that tonal frequency is lessrelated to annoyance at higher background noiselevels but plays a larger role at lower backgroundnoise levels

The data on tonal strength shows that higher tone levelsare linked to higher annoyance ratings analysis of the datato determine a threshold of annoyance is presented inFrancis et als study33 Noise sensitivity was expected tobe associated with annoyance but did not demonstrate sta-tistically significant effects in the ANOVA analysis as abetween-subjects factor This is attributed to the limitednumber of subjects in the study which was selected basedon a power analysis of previous annoyance results ratherthan noise sensitivity results

33 Correlations of Noise Metrics withSubjective Responses

The previous section showed that physical aspects ofthe noise signals (specifically loudness and tonality) werecorrelated with annoyance responses in this sectionassorted metrics for quantifying those physical aspectsare tested against the subjective responses Spearmansnonparametric correlation coefficients were calculatedbetween a number of noise metrics and the average par-ticipants perception ratings of loudness annoyance andTLX-avg The results have been analyzed in two ways

Fig 7mdashMean annoyance perception ratings plotted against (a) Background noise level (b) Tonalfrequency (c) Strength of the tones where Tone 1 indicates the least prominent tone andTone 5 indicates the most prominent tone and (d) Interaction of background noise leveland tonal frequency Error bars indicate one standard error

78 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

first with all twenty tonal signals included and then withthe average ratings for ten signals grouped separately bythe broadband background noise level (40 or 55 dBA)Table 4 presents correlation coefficients between all noisemetrics with the subjective perception responses

When analyzing all signals the noise metric thatdemonstrates the highest correlation coefficients withthe perceived loudness annoyance and TLX-avg rat-ings is ANSI loudness level Other loudness metricswere also significantly correlated to the perception rat-ings but the tonality metrics such as prominence ratiotone-to-noise ratio tonality audibility and Aures tonal-ity did not statistically correlate or had lower coefficientsthan loudness metrics This confirms that loudness is themost dominant factor in determining subjective percep-tion of noise

When the signals are grouped separately by broadbandbackground noise levels though tonality metrics didshow higher correlations with subjective ratings thanloudness metrics The coefficient values for the assortedtonality metrics are all very similar with no particularmetric clearly performing better than others Howeverwhen only looking for correlation coefficients with an-noyance tonal audibility showed slightly higher corre-lation coefficients than other tonality metrics (0888for 40 dBA BNL and 0891 for 55 dBA BNL) Aurestonality also showed high correlation with annoyancefrom 55 dBA BNL signals (0903) but it showed lowercorrelation than other metrics with 40 dBA BNL sig-nals (0709) The results indicate that when the broad-band background noise level is controlled or comparabletonality becomes a more influencing factor on annoyanceevaluation Figure 8 presents scatterplots of the averagedannoyance responses (a) with the ANSI loudness levelacross the entire group and (b) with tonal audibility sepa-rated by background noise level

For all cases combined metrics such as the JointNordic Method tone-corrected perceived noise leveland sound quality indicator did not show remarkablybetter performance than loudness metrics even thoughthese combined metrics were significantly related withannoyance ratings The results suggest that imposingpenalty values to loudness levels based on tonal strengthmay not be the most appropriate way to quantify overallsubjective annoyance of tonal noise Instead using sepa-rate metrics to account for tonality and loudness of build-ing mechanical noises is an effective way to relate to thesignals annoyance

34 Regression Model between Noise Metricsand Annoyance

Based on the results in Table 4 ANSI loudness leveland tonal audibility were selected to be used as predictors

Table 4mdashSpearmans correlation analysis of noisemetrics against subjective responses andSudoku puzzle task performance The resultsare analyzed first with all signals includedand then in two groups separated by back-ground noise level (40 or 55 dBA) Boldedvalues indicate metrics chosen for use inthe regression model based on their overallhigh significant correlation values

All signals (40 dBA and 55 dBA BNL)

Loudness Annoyance TLX-avgPR 0150 0186 0147TNR 0123 0081 0095ΔLta 0006 0056 0019Aures 0297 0359 0314dB 0805 0824 0772dBA 0866 0887 0842ANSI loudness 0946 0950 0926ISO loudness 0938 0952 0925PNL 0892 0920 0886PNLT 0869 0877 0826JNM 0840 0869 0818SQI 0904 0899 0856

40 dBA BNL only

PR 0794 0867 0782TNR 0794 0867 0782ΔLta 0778 0888 0815Aures 0673 0709 0697dB 0806 0939 0855dBA 0794 0927 0830ANSI loudness 0685 0745 0697ISO loudness 0685 0745 0697PNL 0685 0842 0867PNLT 0794 0830 0758JNM 0794 0927 0830SQI 0806 0806 0709

55 dBA BNL only

PR 0799 0867 0758TNR 0709 0845 0845ΔLta 0787 0891 0818Aures 0781 0903 0782dB 0715 0756 0530dBA 0707 0770 0564ANSI loudness 0878 0855 0709ISO loudness 0817 0867 0697PNL 0720 0806 0539PNLT 0744 0782 0527JNM 0707 0770 0564SQI 0689 0663 0444

Correlation is significant at the 001 level (2-tailed)Correlation is significant at the 005 level (2-tailed)

79Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

for a linear multiple regression model for annoyancebecause these two metrics resulted in among the stron-gest correlations with annoyance perception compared

to other noise metrics Equation (2) presents the multi-variate regression model with ANSI loudness level andtonal audibility

Annoyance frac14 1806 thorn 1164 ANSI Loudness soneeth THORNfrac12 thorn 0072 Tonal Audibility dBeth THORNfrac12

eth2THORN

Table 5 also presents standard error of coefficientsstandardized coefficients and statistical significance whenANSI loudness level was only used (in step 1) and whentonal audibility was also included (in step 2) in additionto the coefficient values for each predictor Standardizedb values indicate the number of standard deviations thatthe outcome annoyance will change as a result of onestandard deviation change in the predictor The R2 valuefor the first step model is 0943 which is a measure ofgoodness-of-fit of linear regression indicating that 943of the annoyance rating variance can be explained by theANSI loudness model only When including tonal audibil-ity as a second predictor the R2 value increased to 0962Even though this increase is small the multivariate re-gression model does significantly predict more variationin annoyance perception when including tonal audibilityas a second predictor for step 2 the ANSI loudnesslevel [t(17) = 20796 p lt 0001] and tonal audibility[t(17) = 2943 p = 0009] are both significant predictorsof annoyance Figure 9 illustrates a regression line withthe calculated linear model

The results of the correlation analysis and regressionmodel presented in this paper are in line with the find-ings from More and Daviesrsquo study8 which focused onaircraft flyover noise rather than building mechanicalsystem noise Their work focused only on annoyanceand used metric values that were exceeded some per-centage (often 5) of the time since their flyover

Fig 8mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals against (a) ANSIloudness level for all signals and(b) Tonal audibility for 40 and 55 dBABNL separately Dashed lines indicateregression lines of annoyance ratingprediction with regard to each metric

Table 5mdashLinear regression model of predictors for annoyance perception with 95 bias corrected and accel-erated confidence intervals reported in parentheses Confidence intervals and standard errors arebased on 1000 bootstrap samples Standardized b values indicate the number of standard deviationsthat the outcome annoyance will change as a result of one standard deviation change in the predictor

b Standarderror B

b p

Step 1Constant 3254(2305 4310) 0512 p = 0001ANSI loudness (sone) 1137(1004 1263) 0066 0971 p = 0001

Step 2Constant 1806(0498 3187) 0683 p = 0020ANSI loudness (sone) 1164(1043 1308) 0069 0994 p = 0001Tonal audibility (dB) 0072(0027 0111) 0021 0141 p = 0004

Note 0943 for Step 1 ΔR2 = 0019 for Step 2

80 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

signals varied in time They demonstrated that the bestregression model when using existing metrics to matchtheir annoyance responses included both a loudnessmetric and a tonality metric and they proposed revi-sion to the penalty values used for the Joint NordicMethod to improve its individual predictive capabilityThe authors feel though that it is not necessary tomodify a combined metric (like Joint Nordic Method)since using individual metrics for loudness and for to-nality in the proposed regression model herein demon-strated high correlations to the annoyance responseson their own

4 SUMMARYAND CONCLUSION

The purpose of this study was to investigate hownoise signals with varying degrees of prominent tonessimilar to those produced by building mechanicalequipment affect subjective annoyance perception andtask performance and to develop a prediction modelof annoyance using current noise metrics Subjectscompleted Sudoku puzzles and a questionnaire modi-fied from the NASA task load index to quantify theoverall workload caused by building mechanical noisein this study No statistically significant effect was foundbetween the tonal signals used in this study and task per-formance although there was a trend of decreasing accu-racy with increasing tone strengths based on correctSudoku puzzle answers The validity of the modified taskload index questionnaire was high based on its reliabilitycoefficient and testndashretest coefficient and the average re-sponse from the questionnaire was found to significantlycorrelate with perceived annoyance and loudness of thebackground noise signals A factorial repeated measure

ANOVA revealed that participants felt more annoyed withincreasing background noise level lower tone frequencyand higher tone strength Correlation analysis with noisemetrics and subjective perception ratings found that ANSIloudness level among all other loudness metrics correlatesmost strongly with annoyance perception while assortedtonality metrics showed relatively weaker but still statisti-cally significant correlations with annoyance A statisticallysignificant multivariate regression model with ANSI loud-ness level and tonal audibility has been developed whichdemonstrates an R2 value of 0962

While noise sensitivity of test subjects was surveyedno statistically significant relations between perceptionor performance results and noise sensitivity were foundlikely due to the limited number of test subjects Futurework in this area is suggested with more test subjectsand more tonal signals to understand better the roleof noise sensitivity Also tonal noises from actual buildingmechanical systems often demonstrate multiple tones whichmay be inharmonic or which can fluctuate in time addi-tional investigations using tonal signals that incorporatethese other factors are recommended

5 ACKNOWLEDGMENTS

Many thanks to the undergraduate research assis-tants Adam Steinbach and Kristin Hanna who assistedwith the study design subjective testing and data analysisPartial funding for this project came from an Institute ofNoise Control Engineering Undergraduate Research Grant

6 REFERENCES

1 International Organization for Standardization (ISO) ldquoAcousticsmdashDescription Measurement and Assessment of EnvironmentalNoisemdashPart 2 Determination of Environmental Noise LevelsrdquoInternational Standard ISO 1996ndash22007 International Organi-zation for Standardization (2007)

2 ldquoSeattle Municipal Code Chapter 2508 Noise Controlrdquo Seattle(2007)

3 ldquoNoise Control Ordinance of the County of Los Angelesrdquo LosAngeles County (1978)

4 ldquoOrdinance of Minnesota Chapter 389 ndash Noiserdquo Minnesota(2008)

5 NY ADC LAW 27-770 NY code ndash Section 27-770 ldquoNoiseControl of Mechanical Equipmentrdquo New York (2006)

6 RP Hellman ldquoGrowth rate of loudness annoyance and noisi-ness as a function of tone location within the noise spectrumrdquoJ Acoust Soc Am 75(1) 209ndash218 (1984)

7 RP Hellman ldquoPerceived magnitude of two-tone-noise com-plexes loudness annoyance and noisinessrdquo J Acoust SocAm 77(4) 1497ndash1504 (1985)

8 S More and P Davies ldquoHuman responses to the tonalness of air-craft noiserdquo Noise Control Engr J 58(4) 420ndash440 (2010)

9 KH Lee P Davies and AM Surprenant ldquoQuantification ofthe tonal prominence of complex tones in machinery noiserdquoNoiseCon04 (2004)

Fig 9mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals plotted against theproposed linear regression model ofannoyance perception (dashed) basedon ANSI loudness level and tonalaudibility (R2 = 096)

81Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

10 A Hastings H Lee P Davies and AM Surprenant ldquoMeasure-ment of the attributes of complex tonal components commonlyfound in product soundrdquo Noise Control Engr J 51(4) 195ndash209(2003)

11 EE Ryherd and LM Wang ldquoImplications of human perfor-mance and perception under tonal noise conditions on indoornoise criteriardquo J Acoust Soc Am 124(1) 218ndash226 (2008)

12 P Susini S McAdams S Winsberg I Perry S Vieillard andX Rodet ldquoCharacterizing the sound quality of air-conditioningnoiserdquo Appl Acoust 65(8) 763ndash790 (2004)

13 B Berglund P Hassmeacuten and A Preis ldquoAnnoyance and spectralcontrast are cues for similarity and preference of soundsrdquo J SoundVibr 250(1) 53ndash64 (2002)

14 U Landstroumlm E Aringkerlund A Kjellberg and M Tesarz ldquoEx-posure levels tonal components and noise annoyance in work-ing environmentsrdquo Environ Int 21(3) 265ndash275 (1995)

15 U Landstroumlm A Kjellberg and M Bystroumlm ldquoAcceptablelevels of sounds with different spectral characteristics duringthe performance of a simple and a complex non-auditory taskrdquoJ Sound Vibr 160(3) 533ndash542 (1993)

16 D Laird ldquoThe influence of noise on production and fatigue asrelated to pitch sensation level and steadiness of the noiserdquo JAppl Psychol 17(3) 320ndash330 (1933)

17 JV Grjmaldi ldquoSensori-motor performance under varying noiseconditionsrdquo Ergonomics 2(1) 34ndash43 (1958)

18 K Holmberg U Landstrom and A Kjellberg ldquoEffects of ven-tilation noise due to frequency characteristic and sound levelrdquoJ Low Freq Noise Vib 12(4) 115ndash122 (1993)

19 International Organization for Standardization (ISO) ldquoAcousticsmdashAssessment of noise annoyance by means of social and socio-acoustic surveysrdquo International Standard ISOTS 15666 2003International Organization for Standardization (2003)

20 TH Pedersen ldquoThe ldquoGenlydrdquo noise annoyance model dose-response relationships modelled by logistic functionsrdquo DELTAHoslashrsholm Danmark (2007)

21 CMarquis-Favre E Premat andDAubreacutee ldquoNoise and its effectsmdasha review on qualitative aspects of sound Part II noise and annoy-ancerdquo Acta Acust united with Acust 91(4) 626ndash642 (2005)

22 American National Standards Institute (ANSI) ldquoAcousticsmdashMeasurement of Airborne Noise Emitted by Information Technol-ogy and Telecommunications EquipmentmdashPart 1 Determinationof Sound Power Level and Emission Sound Pressure LevelrdquoAmerican National Standards Institute ANSIASA S1210-2010Part 1 American Society of America (2010)

23 W Aures ldquoThe sensory euphony as a function of auditory sen-sationsrdquo Acustica 58(5) 282ndash290 (1985)

24 American National Standards Institute (ANSI) ldquoProcedure forthe Computation of Loudness of Steady Soundsrdquo AmericanNational Standards Institute ANSI S34-2007 Acoustical Soci-ety of America (2007)

25 ldquoAcousticsmdashMethod for Calculating Loudness Levelrdquo Interna-tional Standard ISO 5321975 International Organization forStandardization (1975)

26 E Zwicker ldquoSubdivision of the audible frequency range intocritical bands (Frequenzgruppen)rdquo J Acoust Soc Am 33(2)248ndash248 (1961)

27 BR Glasberg and BCJ Moore ldquoPrediction of absolutethresholds and equal loudness contours using a modified loud-ness modelrdquo J Acoust Soc Am 120 585ndash588 (2006)

28 Federal Aviation Administration ldquoPart 36 Noise StandardsAircraft Type and Airworthiness Certificationrdquo Federal Avia-tion Regulations Federal Aviation Administration (1969)

29 Air-Conditioning Heating and Refrigeration Institute (AHRI)ldquoSound Quality Evaluation Procedures for Air-Conditioningand Refrigeration Equipmentrdquo Air-Conditioning Heating andRefrigeration Institute AHRIANSI 1140-2012 Air-ConditioningHeating and Refrigeration Institute (2012)

30 WE Blazier ldquoRevised noise criteria for application in theacoustical design and rating of HVAC systemsrdquo Noise ControlEngr 16(2) 64ndash73 (1981)

31 F Faul E Erdfelder A Buchner and A-G Lang ldquoStatisticalpower analyses using GPower 31 tests for correlation and re-gression analysesrdquo Behav Res Methods 41 1149ndash1160 (2009)

32 M Schutte A Marks E Wenning and B Griefahn ldquoThe de-velopment of the noise sensitivity questionnairerdquo Noise Heal9(34) 15 (2007)

33 J Francis J Lee A Steinbach and LM Wang ldquoDeterminingannoyance thresholds of tones in noiserdquo ASHRAE Trans120 (2014)

34 JW Grabbe ldquoSudoku and working memory performance forolder adultsrdquo Act Adapt Aging 35(3) 241ndash254 (2011)

35 HS Chang and JM Gibson ldquoThe oddndasheven effect in Sudokupuzzles effects of working memory aging and experiencerdquoAm J Psychol 124(3) 313ndash324 (2011)

36 ldquoNASA TLX Task Load Indexrdquo [Online] Available at httphumansystemsarcnasagovgroupstlx (accessed September 92015)

37 RFS Job J Hatfield NL Carter P Peploe R Taylor andS Morrell ldquoGeneral scales of community reaction to noise(dissatisfaction and perceived affectedness) are more reliablethan scales of annoyancerdquo J Acoust Soc Am 110(2) 939ndash946(2001)

82 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

serious sources of public noise pollution since the 1960sIn the 1980s Hellman found that tonal components inbroadband spectra impact ratings of annoyance loudnessand noisiness6 and that the number of tones and frequencydifferences between tones as well as the frequency of thetone itself influence annoyance7 More and Davies8 alsoexamined the effects of tones on human annoyance fromaircraft flyover noise using a questionnaire without anyaccompanying tasks They found that regression modelsthat include metrics for both loudness and tonality matchedwell with annoyance responses from their time-varyingsignals Lee et al9 investigated the tonality perceptionfor harmonic complex tones and pointed out the diffi-culties of quantifying overall tonality including harmonictones with existing methods Hastings et al10 investigatedassorted tonality metrics for predicting tonality and an-noyance of noises They proposed modifications incalculating the existing metrics and suggested that thebandwidth and roll-off rate of tones should be includedfor accurate tonality perception for aircraft noise

More recent attention has focused on perception oftones in noise from building machinery Ryherd andWang11 investigated assorted building mechanical noisesamples and showed that current indoor noise criteria werenot accurately reflecting annoyance because the criteria donot typically account for tonal characteristics in assess-ment Susini et al12 used multidimensional scaling analy-sis to find that one of the most important sound qualitydimensions of noise from indoor air-conditioning unitsis the ratio of tonal harmonic components to broadbandnoise components Berglund et al13 also investigated per-ception of environmental noises including ventilation-likenoise spectra with the multidimensional scaling methodol-ogy and concluded that spectral contrast which is relatedto the tonality is the best acoustic index for predicting thepreference rating of noises Besides laboratory studiesLandstroumlm et al14 explored noise levels and annoyanceby occupants in actual working spaces They found thatthe relation between noise levels and annoyance wasweak but annoyance ratings were significantly increasedwhen tones were present in the noise These previousstudies strongly suggest that tonality metrics should be in-cluded when evaluating noise from mechanical systems inbuildings but to date none of existing tonal metrics is uti-lized broadly and there is still limited understanding inlinking measurable objective metrics to annoyance

Besides annoyance how tones in noise impact taskperformance is also of interest Previous research find-ings into effects of tones on human performance havebeen inconsistent and limited Landstroumlm et al1415 foundthat task performance was significantly lower for tonalnoises and Laird16 argued that tones above 512 Hz havea greater effect on increasing error rates of tasks in arti-ficial factory experiments Grjmaldi17 also found tendencies

of slower response times and increasing error rates of coor-dinated movement performance for tones in the rangeof 2400 to 4800 Hz However a few other studies1118

did not find any statistically significant differences intask performance between broadband and tonal noises

This paper describes a subjective investigation on howexposure to tonal noise as produced by building mech-anical systems impacts human annoyance and task per-formance using a larger variety of signals than mostprevious studies The relationships between a number ofknown noise metrics objectively describing both loud-ness and tonality and annoyance responses are examinedResults are also used to develop a preliminary annoyanceprediction model through statistical analysis based on anoise signals loudness and tonalityWhile harmonic struc-tures of tones have been shown to impact annoyance andother psychoacoustic qualities such as sharpness rough-ness or fluctuation may play a part as well those aspectswere not directly considered in this investigation Ratherthis study focused on how these two primary characteris-tics of loudness and tonality affect annoyance and perfor-mance because previous studies pointed out that thetonality impulsivity and loudness have the most influen-tial impacts on listeners responses

There is a degree of uncertainty in defining annoyancedue to noise ISOTS 156662003 defines noise-inducedannoyance as ldquoone persons individual adverse reaction tonoise in various ways including dissatisfaction bother an-noyance and disturbancerdquo19 While a variety of definitionsfor annoyance have been suggested it is generally agreedthat annoyance is concerned with physical noise character-istics the context of measurement and personal attributesof listeners20 In this study the physical noise characteris-tics of interest are loudness and tonality Although thesubjective testing has been conducted in a controlled lab-oratory the context of the measurement is meant to be likean office environment From reviewing previous researchstudies Marquis-Favre et al21 indicated that among non-acoustic factors that can influence annoyance fear andnoise sensitivity were found to have the most significanteffects In the investigation discussed herein fear was notconsidered since listeners are not expected to fear regularlevels of building mechanical noise but noise sensitivitywas surveyed as a personal attribute

The noise metrics investigated in this paper that havebeen developed to quantify tonality or the degree to whichtones are present in broadband noise are reviewed ANSIS1210-2010Part1 Annex D22 presents tone-to-noise ratio(TNR) and prominence ratio (PR) to quantify tonality andISO1996-22007AnnexC5 suggests tonal audibility (ΔLta)These metrics are calculated from the steady-state fre-quency spectrum of the noise recording through digitalfast Fourier transform analysis There are two main dif-ferences between tonal audibility and the previous two

72 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

metrics tone-to-noise ratio and prominence ratio Onemajor difference is that tonal audibility uses A-weightedsound pressure levels and includes a frequency correctionterm in its calculation so that the prominence criteria oftones is constant across frequencies whereas TNR andPR ratings are based on unweighted sound pressure levelsConsequently the prominence of tones is frequency de-pendent for TNR and PR ratings but not for ΔLta Thatis PR = 5 for a 100Hz tone is not necessarily the same per-ceived tonality as PR = 5 for a 500 Hz tone The other dif-ference is that the tonal audibility uses a linear regressionline instead of actual noise components when calculatingmasking tonal levels within the critical bands The equa-tion to calculate ΔLta is given by

ΔLta frac14 Lpt Lpn thorn 2 dBthorn log 1thorn fc502

25

eth1THORN

where Lpt is the total sound pressure level of the tones Lpnis the total sound pressure level of the masking noise in thecritical band and fc is the center frequency of the criticalband Based on the tonal audibility calculation penaltyfactors between 0 and 6 dB are provided to adjust the over-all A-weighted noise levels rather than setting prominencecriteria It also requires separate analysis for each tonewithin a multi-tonal noise signal Aures tonality (Aures)is another metric for tonality that considers the frequencyas well as bandwidth and levels of all tonal componentsthrough use of weighting functions23 It is one of the fewthat can account for multiple tones in a signal

Popular loudness metrics are also investigated in thisstudy because previous studies have found that loudnessof the noise is the most relevant feature correlating to an-noyance besides tonality Among the included loudnessmetrics are A-weighted (dBA) and unweighted (dB)equivalent sound pressure levels and stationary loudnesslevels calculated according to ANSI S34-200724 (ANSIloudness) and ISO 532-1975 B method25 (ISO loudness)The ISO loudness and ANSI loudness are based onZwickers26 and Glasberg and Moores27 loudness mod-els respectively They both can use stationary one-thirdoctave band data for the calculation of loudness

A few noise metrics that consider both loudness andtonality to produce an overall rating for tonal noiseshave been proposed These combined metrics basicallyadd penalty values to the loudness levels due to thepresence of tones The Joint Nordic Method (JNM) isstandardized in ISO 1996-220071 where the penaltyk values are derived from tonal audibility and addedto A-weighted sound pressure level Perceived noiselevel (PNL) was implemented to quantify subjective an-noyance of aircraft noise calculated from one-third oc-tave band values tone-corrected perceived noise level

(PNLT) is a revised version of PNL with the additionof a tone correction factor28 Sound quality indicator (SQI)is a similar metric suggested by the Air-ConditioningHeating and Refrigeration Institute to rate the sound qual-ity of building mechanical product noise based on one-third octave bands29 but it has yet to be applied widely

2 METHODOLOGY

21 Test Laboratory

The subjective testing was completed in an acoustictesting chamber at the University of Nebraska Figure 1illustrates a schematic plan of the testing chamber whichhas a volume of approximately 278 m3 The chamber isacoustically isolated from a monitor room and nearbyspaces Materials in the room include carpeted floorgypsum board walls with additional absorptive panelsacoustic bass traps and acoustical ceiling tiles The av-erage mid-frequency reverberation time is 031 secondsand the ambient background noise level is 37 dBAwhenair-conditioning in the chamber is turned off Figure 2presents the ambient background noise levels in thechamber across octave bands The tonal test signals weregenerated through a ceiling-mounted Armstrong i-ceilingspeaker and a sub-woofer in a corner The i-ceiling speakerappears as other ceiling tiles in the ceiling grid so thatparticipants cannot visually identify the location of thesound source Participants sat in the middle of the cham-ber and were advised not to move their location duringthe experiment

22 Test Signals

A total of 22 noise signals were generated for use inthis study by the program Test Tone Generator fromEsser Audio Two levels of broadband noise without any

Fig 1mdashSchematic plan of the acoustic testingchamber at the University of Nebraska

73Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

tonal components were used either 40 or 55 dBA overallfollowing a 5 dBoctave room criteria (RC) contour30These two levels are in the range of common backgroundnoise levels found in buildings A single tone at one oftwo frequencies and at one of five prominence levelswas added separately to the broadband noise signalsto create the other 20 noise signals The two tonal fre-quencies were selected to be 125 Hz which is a com-mon tone generated by building mechanical equipmentand 500 Hz as it is slightly higher but still in the fre-quency range where a number of other building mechan-ical equipment exhibit tones The five tone levels wereselected to range from below to above the prominencethresholds listed in ANSI S1210-201022 PR = 18 dBfor 125 Hz and PR = 12 dB for 500 Hz Table 1 pre-sents the prominence ratio values for each test signalFigure 3 illustrates the one-third octave band spectra ofthe test signals All tonal signals were measured using a BampK 4189-A microphone through the BampK PULSE sys-

tem at the listeners ear position in the testing chamberand averaged over a minute for calculation of noise metricsThe metrics were calculated using Matlab or programs pro-vided by the associated standards

23 Test Participants and Procedure

Ten participants four females and six males wererecruited from the University of Nebraska mdash Omahacommunity ranging in age from 25 to 43 years oldThe University of Nebraska mdash Lincoln InstitutionalReview Board approved the study and each participantwas paid for their time The sample size was determinedby a priori power analysis using the effect size from Moreand Davies8 statistical results using GPower version3131 The effect size for multiple regression modelsCohens f 2 was calculated as 669 from the squared mul-tiple correlation values in the previous study The minimum

Fig 2mdashMeasured octave band spectra for theambient background noise in the testchamber when air-conditioning is off

Table 1mdashProminence ratios for the tones in the noisestimuli used in the subjective testing aslisted by tonal frequency broadband back-ground noise level and tone level

Frequency(Hz)

BNL(dBA)

Prominence ratio (dB)

Tonelevel1

Tonelevel2

Tonelevel3

Tonelevel4

Tonelevel5

125 40 15 18 21 24 2755 13 15 18 21 24

500 40 9 12 15 18 2155 6 9 12 15 18

Fig 3mdashMeasured one-third octave bandspectra for a few of the test noisesignals (a) Broadband 40 dBA signaland some with assorted tones(b) Broadband 55 dBA signal andsome with assorted tones Tones wereeither at 125 or 500 Hz for clarity onlythe lowest and highest tonal strengthsare presented

74 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

sample size was then found to be six participants to achieve80 power (1 b) at two-sided 5 significance level (a)Based on this finding and available research funds a test-ing plan was designed to assess 22 signals across tentest subjects

All participants completed an orientation session includ-ing a hearing screening test before participation and dem-onstrated normal hearing with thresholds below 25 dBhearing level (HL) from 125 Hz to 8 kHz The noisesensitivity of each participant was also measured by areduced version (13 items only) of the Noise-Sensitivity-Questionnaire (NoiSeQ) by Schutte et al32 during the ori-entation session The participants were asked to answereach item using a four-level rating scale (strongly agree = 1slightly agree = 2 slightly disagree = 3 strongly dis-agree = 4) The responses were averaged across all itemsto form a composite scale to quantify the noise sensitivityfor each participant

The main test consisted of two parts a direct assess-ment with task (part A) and a magnitude adjustment test(part B) The results of part B have been presented inanother paper33 and hence are not included herein In partA participants were asked to complete as many Sudokunumber puzzles as possible while exposed to a broad-band noise signal some with assorted tonal compo-nents for 10 minutes Sudoku puzzles were selected asthe measure of task performance as they are compact toadminister easy to explain to test participants and havebeen used as a measure of task performance in otherstudies with results showing significant relationship withworking memory3435 All participants practiced solvingSudoku puzzles during the orientation session before par-ticipating in the main test and the difficulty of all Sudokupuzzles in the main test was held constant The puzzleswere all nine by nine with forty of the eighty-one grids be-ing prefilled with numbers

After spending 10 minutes solving the Sudoku puz-zles the subjects answered five questions on a subjec-tive questionnaire about the noise they had just heardThe questionnaire was a modified version of the NASAtask load index36 The original NASA task load index isdivided into six subscales mental demand physical de-mand temporal demand performance effort and frus-tration In this study the questions on physical demandtemporal demand and frustration were not included in-stead questions were added on rating loudness and annoy-ance incurred by noise as shown in Table 2 Participantsresponded to each question based on a 21-point scale (tomatch the scale from the original NASA task load index)on a paper form

Part A consisted of ten 30-minute sessions that werecompleted by each subject individually on differentdays Within each 30-minute session subjects were ex-posed to three noise signals (each for 10 minutes) and

thus completed three sequences of Sudoku puzzles (dif-ferent puzzles each time) followed by the questionnaireTo minimize the influence of back-to-back comparisonsof tonal noise conditions a neutral background noisecondition without any tonal components was used asthe second signal within each 30-minute test sessionWithin a single 30-minute test session the noise levelof the broadband noise without consideration for anytonal components remained at a constant level either40 or 55 dBA The presentation order of the backgroundnoise levels and tonal test signals was carefully balancedacross all subjects using a Latin square design

Two task performance measures were gathered by(1) counting the amount of Sudoku puzzles a subjectcompleted within a 10-minute trial with partial comple-tions included as well and (2) quantifying the accuracyof the puzzle answers in terms of correct numbers amongthose answered in a puzzle The maximum and mini-mum number of Sudoku puzzles participants completedin one 10-minute session were 28 and 03 and the max-imum and minimum accuracy of the puzzles were 100and 69

3 RESULTS AND DISCUSSION

The reliability of each participants responses wasdetermined from correlation analysis of the participantsindividual annoyance responses to a loudness metrictonality metric and average ratings across participants8Figure 4 presents correlation coefficients of each parti-cipants annoyance responses to the ANSI loudnesslevel tonal audibility and mean values across partici-pants Two participants responses (number 6 and 8)were excluded from all analyses because they ratedresponses randomly regardless of sound characteristics(correlation value lt02) The subject-to-loudness co-efficient of participant 6 was 017 and the subject-to-

Table 2mdashItems from the subjective questionnaireas modified from the NASA task loadindex

Description Questions

Mental demand 1 How mentally demandingwas the task

Overall performance 2 How successful wereyou in accomplishing whatyou were asked to do

Effort 3 How hard did you haveto work to accomplish yourlevel of performance

Loudness 4 How loud was the noiseAnnoyance 5 How annoying was the noise

75Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

tonality correlation coefficient of participant 8 was 003All subsequent analyses are based on the remaining eightsubjects which is still above the minimum sample size ofsix recommended from the a priori power analysis Addi-tionally outlier responses of annoyance and task perfor-mance scores were excluded from the statistical analysispresented below The outliers were identified using the cri-terion of being beyond three standard deviations from theaverage across participants Two outliers of annoyance rat-ings and three outliers of task performance scores wereexcluded for analyses based on this criterion

31 Relating Task Performance to SubjectiveResponses and to Noise Attributes

The task performance measures related to the Sudokupuzzles were correlated to the participants subjective

responses on the modified NASA task load index ques-tionnaires (Table 3) Spearmans correlation (r) was utilizedbecause not all of the variables met the assumption ofhaving a normal distribution with the sample size uti-lized An additional ldquoTLX-avgrdquo score was calculated asthe averaged value of all five items from the modified sur-vey to represent an overall rating of subjective task loadperception induced by noise exposure Since the task diffi-culty was held constant with equivalently difficult Sudokupuzzles throughout the experiment the variations in sub-jective ratings observed within subjects can be consideredas the result of varying background noise conditions Jobet al37 have recommended against using a single questionitem about annoyance because of its reduced validity con-sequently the composite modified Noise TLX rating isproposed as an alternative in this laboratory study Witha Cronbachs a coefficient for the reliability of 082 anda testndashretest correlation of the Noise TLX measure forthe stability of 077 the ldquoTLX-avgrdquo questionnaire wasfound to be internally consistent and stable over timeand thus suitable for the purpose of this test

As Table 3 indicates most of the subjective responseswere significantly correlated with each other Specificallyof interest the mental demand responses showed highcorrelations with perceptions of loudness and annoy-ance of the noise and as expected loudness and annoy-ance ratings were significantly correlated with each other(r = 0948) The only statistically significant correla-tion between a task performance result and a subjec-tive response was between ldquoaccuracyrdquo (accuracy ratesof participants puzzle answers) and responses to theldquoperformancerdquo question on the questionnaire (r =0483)

Figures 5 and 6 present the averaged task perfor-mance of the accuracy and number of completed puzzles

Fig 4mdashCorrelation coefficients of eachparticipants annoyance responses toeach signals ANSI loudness tonalaudibility and group average

Table 3mdashSpearmans correlation analysis of the subjective responses and Sudoku puzzle task performancemeasures TLX-avg is the average value of the responses to all five questions on the modified taskload index questionnaire ldquoNo of completedrdquo refers to the number of completed puzzles for eachtrial and ldquoaccuracyrdquo indicates accuracy rates of participants puzzle answers

Mentaldemand

Performance Effort Loudness Annoyance TLX-avg

No ofcompleted

Accuracy

Mental demand ndashPerformance 0260 ndashEffort 0610 0496 ndashLoudness 0501 0105 0230 ndashAnnoyance 0528 0162 0398 0948 ndashTLXndashavg 0631 0374 0601 0880 0956 ndashNo of puzzles completed 0317 0438 0394 0074 0020 0171 ndashAccuracy 0105 0483 0071 0289 0252 0330 0080 ndash

Correlation is significant at the 001 level (2-tailed)Correlation is significant at the 005 level (2-tailed)

76 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

against the physical attributes of the noise signals in-cluding background noise level tone frequency andthe five levels of tone strengths The repeated measureANOVA (analysis of variance) confirms that there wereno statistically significant differences between task per-formances across the various noise attributes Thussubjects did not complete more puzzles or have higheraccuracy under any particular tonal frequency back-ground noise level or tone strength although there

appears to be a slight tendency of lower accuracy withgreater tone strength

32 Relating Noise Attributes toAnnoyance Responses

To understand how the physical aspects of the noisesignals (background noise level tone frequency andtonal strength) related to annoyance a three-way repeated

Fig 5mdashAveraged accuracy of Sudoku puzzle answers as task performance scores plotted against(a) Background noise level (b) Tonal frequency and (c) Strength of the tones where Tone1 indicates the least prominent tone and Tone 5 indicates the most prominent tone Errorbars indicate one standard error

Fig 6mdashAveraged number of completed Sudoku puzzles as task performance scores plotted against(a) Background noise level (b) Tonal frequency and (c) Strength of the tones where Tone1 indicates the least prominent tone and Tone 5 indicates the most prominent tone Errorbars indicate one standard error

77Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

measure ANOVAwas conducted Mauchlys test indicatedthat the assumption of sphericity had been met for themain effects of tonal strength and its interactions withtone frequency and background noise level The analysisshows a significant main effect of background noise level[F(17) = 8261 p lt 0001 2p = 092] tone frequency

[F(17) = 2001 p = 0003 2p = 074] and tonal strength

[F(428) = 476 p = 0005 2p = 041] on annoyanceThe main analysis shows that the 55 dBA based

tonal signals were significantly more annoying than40 dBA based tonal signals and that the 125 Hz tonalsignals were significantly more annoying than 500 Hztonal signals Contrast comparisons reveal that the 4thhighest [F(17) = 10420 p = 0014] and 5th highest[F(17) = 12069 p = 0010] in prominence tonal signalswere perceived as more annoying than the least (1st)prominent tonal signals

Figure 7 illustrates the mean annoyance ratings acrossbackground noise levels tonal frequencies and tonestrengths Summarizing these results the overall back-ground noise level does impact annoyance with higherlevels leading to greater annoyance The lower fre-quency tone generated greater annoyance ratings butone should note that the prominence levels of the125 Hz tone versus those of the 500 Hz tone used inthe study were not the same even though the relativedifferences from the threshold of tones presented inISO 1996-22007 are the same There was also a sig-nificant interaction effect between background noiselevel and tone frequency [F(17) = 3331 p = 0014

2p = 060] As plotted in Fig 7(d) the difference be-tween annoyance ratings of the 125 and 500 Hz toneswas greater with the 40 dBA background noise levelcondition than with the 55 dBA background noiselevel condition It appears that tonal frequency is lessrelated to annoyance at higher background noiselevels but plays a larger role at lower backgroundnoise levels

The data on tonal strength shows that higher tone levelsare linked to higher annoyance ratings analysis of the datato determine a threshold of annoyance is presented inFrancis et als study33 Noise sensitivity was expected tobe associated with annoyance but did not demonstrate sta-tistically significant effects in the ANOVA analysis as abetween-subjects factor This is attributed to the limitednumber of subjects in the study which was selected basedon a power analysis of previous annoyance results ratherthan noise sensitivity results

33 Correlations of Noise Metrics withSubjective Responses

The previous section showed that physical aspects ofthe noise signals (specifically loudness and tonality) werecorrelated with annoyance responses in this sectionassorted metrics for quantifying those physical aspectsare tested against the subjective responses Spearmansnonparametric correlation coefficients were calculatedbetween a number of noise metrics and the average par-ticipants perception ratings of loudness annoyance andTLX-avg The results have been analyzed in two ways

Fig 7mdashMean annoyance perception ratings plotted against (a) Background noise level (b) Tonalfrequency (c) Strength of the tones where Tone 1 indicates the least prominent tone andTone 5 indicates the most prominent tone and (d) Interaction of background noise leveland tonal frequency Error bars indicate one standard error

78 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

first with all twenty tonal signals included and then withthe average ratings for ten signals grouped separately bythe broadband background noise level (40 or 55 dBA)Table 4 presents correlation coefficients between all noisemetrics with the subjective perception responses

When analyzing all signals the noise metric thatdemonstrates the highest correlation coefficients withthe perceived loudness annoyance and TLX-avg rat-ings is ANSI loudness level Other loudness metricswere also significantly correlated to the perception rat-ings but the tonality metrics such as prominence ratiotone-to-noise ratio tonality audibility and Aures tonal-ity did not statistically correlate or had lower coefficientsthan loudness metrics This confirms that loudness is themost dominant factor in determining subjective percep-tion of noise

When the signals are grouped separately by broadbandbackground noise levels though tonality metrics didshow higher correlations with subjective ratings thanloudness metrics The coefficient values for the assortedtonality metrics are all very similar with no particularmetric clearly performing better than others Howeverwhen only looking for correlation coefficients with an-noyance tonal audibility showed slightly higher corre-lation coefficients than other tonality metrics (0888for 40 dBA BNL and 0891 for 55 dBA BNL) Aurestonality also showed high correlation with annoyancefrom 55 dBA BNL signals (0903) but it showed lowercorrelation than other metrics with 40 dBA BNL sig-nals (0709) The results indicate that when the broad-band background noise level is controlled or comparabletonality becomes a more influencing factor on annoyanceevaluation Figure 8 presents scatterplots of the averagedannoyance responses (a) with the ANSI loudness levelacross the entire group and (b) with tonal audibility sepa-rated by background noise level

For all cases combined metrics such as the JointNordic Method tone-corrected perceived noise leveland sound quality indicator did not show remarkablybetter performance than loudness metrics even thoughthese combined metrics were significantly related withannoyance ratings The results suggest that imposingpenalty values to loudness levels based on tonal strengthmay not be the most appropriate way to quantify overallsubjective annoyance of tonal noise Instead using sepa-rate metrics to account for tonality and loudness of build-ing mechanical noises is an effective way to relate to thesignals annoyance

34 Regression Model between Noise Metricsand Annoyance

Based on the results in Table 4 ANSI loudness leveland tonal audibility were selected to be used as predictors

Table 4mdashSpearmans correlation analysis of noisemetrics against subjective responses andSudoku puzzle task performance The resultsare analyzed first with all signals includedand then in two groups separated by back-ground noise level (40 or 55 dBA) Boldedvalues indicate metrics chosen for use inthe regression model based on their overallhigh significant correlation values

All signals (40 dBA and 55 dBA BNL)

Loudness Annoyance TLX-avgPR 0150 0186 0147TNR 0123 0081 0095ΔLta 0006 0056 0019Aures 0297 0359 0314dB 0805 0824 0772dBA 0866 0887 0842ANSI loudness 0946 0950 0926ISO loudness 0938 0952 0925PNL 0892 0920 0886PNLT 0869 0877 0826JNM 0840 0869 0818SQI 0904 0899 0856

40 dBA BNL only

PR 0794 0867 0782TNR 0794 0867 0782ΔLta 0778 0888 0815Aures 0673 0709 0697dB 0806 0939 0855dBA 0794 0927 0830ANSI loudness 0685 0745 0697ISO loudness 0685 0745 0697PNL 0685 0842 0867PNLT 0794 0830 0758JNM 0794 0927 0830SQI 0806 0806 0709

55 dBA BNL only

PR 0799 0867 0758TNR 0709 0845 0845ΔLta 0787 0891 0818Aures 0781 0903 0782dB 0715 0756 0530dBA 0707 0770 0564ANSI loudness 0878 0855 0709ISO loudness 0817 0867 0697PNL 0720 0806 0539PNLT 0744 0782 0527JNM 0707 0770 0564SQI 0689 0663 0444

Correlation is significant at the 001 level (2-tailed)Correlation is significant at the 005 level (2-tailed)

79Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

for a linear multiple regression model for annoyancebecause these two metrics resulted in among the stron-gest correlations with annoyance perception compared

to other noise metrics Equation (2) presents the multi-variate regression model with ANSI loudness level andtonal audibility

Annoyance frac14 1806 thorn 1164 ANSI Loudness soneeth THORNfrac12 thorn 0072 Tonal Audibility dBeth THORNfrac12

eth2THORN

Table 5 also presents standard error of coefficientsstandardized coefficients and statistical significance whenANSI loudness level was only used (in step 1) and whentonal audibility was also included (in step 2) in additionto the coefficient values for each predictor Standardizedb values indicate the number of standard deviations thatthe outcome annoyance will change as a result of onestandard deviation change in the predictor The R2 valuefor the first step model is 0943 which is a measure ofgoodness-of-fit of linear regression indicating that 943of the annoyance rating variance can be explained by theANSI loudness model only When including tonal audibil-ity as a second predictor the R2 value increased to 0962Even though this increase is small the multivariate re-gression model does significantly predict more variationin annoyance perception when including tonal audibilityas a second predictor for step 2 the ANSI loudnesslevel [t(17) = 20796 p lt 0001] and tonal audibility[t(17) = 2943 p = 0009] are both significant predictorsof annoyance Figure 9 illustrates a regression line withthe calculated linear model

The results of the correlation analysis and regressionmodel presented in this paper are in line with the find-ings from More and Daviesrsquo study8 which focused onaircraft flyover noise rather than building mechanicalsystem noise Their work focused only on annoyanceand used metric values that were exceeded some per-centage (often 5) of the time since their flyover

Fig 8mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals against (a) ANSIloudness level for all signals and(b) Tonal audibility for 40 and 55 dBABNL separately Dashed lines indicateregression lines of annoyance ratingprediction with regard to each metric

Table 5mdashLinear regression model of predictors for annoyance perception with 95 bias corrected and accel-erated confidence intervals reported in parentheses Confidence intervals and standard errors arebased on 1000 bootstrap samples Standardized b values indicate the number of standard deviationsthat the outcome annoyance will change as a result of one standard deviation change in the predictor

b Standarderror B

b p

Step 1Constant 3254(2305 4310) 0512 p = 0001ANSI loudness (sone) 1137(1004 1263) 0066 0971 p = 0001

Step 2Constant 1806(0498 3187) 0683 p = 0020ANSI loudness (sone) 1164(1043 1308) 0069 0994 p = 0001Tonal audibility (dB) 0072(0027 0111) 0021 0141 p = 0004

Note 0943 for Step 1 ΔR2 = 0019 for Step 2

80 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

signals varied in time They demonstrated that the bestregression model when using existing metrics to matchtheir annoyance responses included both a loudnessmetric and a tonality metric and they proposed revi-sion to the penalty values used for the Joint NordicMethod to improve its individual predictive capabilityThe authors feel though that it is not necessary tomodify a combined metric (like Joint Nordic Method)since using individual metrics for loudness and for to-nality in the proposed regression model herein demon-strated high correlations to the annoyance responseson their own

4 SUMMARYAND CONCLUSION

The purpose of this study was to investigate hownoise signals with varying degrees of prominent tonessimilar to those produced by building mechanicalequipment affect subjective annoyance perception andtask performance and to develop a prediction modelof annoyance using current noise metrics Subjectscompleted Sudoku puzzles and a questionnaire modi-fied from the NASA task load index to quantify theoverall workload caused by building mechanical noisein this study No statistically significant effect was foundbetween the tonal signals used in this study and task per-formance although there was a trend of decreasing accu-racy with increasing tone strengths based on correctSudoku puzzle answers The validity of the modified taskload index questionnaire was high based on its reliabilitycoefficient and testndashretest coefficient and the average re-sponse from the questionnaire was found to significantlycorrelate with perceived annoyance and loudness of thebackground noise signals A factorial repeated measure

ANOVA revealed that participants felt more annoyed withincreasing background noise level lower tone frequencyand higher tone strength Correlation analysis with noisemetrics and subjective perception ratings found that ANSIloudness level among all other loudness metrics correlatesmost strongly with annoyance perception while assortedtonality metrics showed relatively weaker but still statisti-cally significant correlations with annoyance A statisticallysignificant multivariate regression model with ANSI loud-ness level and tonal audibility has been developed whichdemonstrates an R2 value of 0962

While noise sensitivity of test subjects was surveyedno statistically significant relations between perceptionor performance results and noise sensitivity were foundlikely due to the limited number of test subjects Futurework in this area is suggested with more test subjectsand more tonal signals to understand better the roleof noise sensitivity Also tonal noises from actual buildingmechanical systems often demonstrate multiple tones whichmay be inharmonic or which can fluctuate in time addi-tional investigations using tonal signals that incorporatethese other factors are recommended

5 ACKNOWLEDGMENTS

Many thanks to the undergraduate research assis-tants Adam Steinbach and Kristin Hanna who assistedwith the study design subjective testing and data analysisPartial funding for this project came from an Institute ofNoise Control Engineering Undergraduate Research Grant

6 REFERENCES

1 International Organization for Standardization (ISO) ldquoAcousticsmdashDescription Measurement and Assessment of EnvironmentalNoisemdashPart 2 Determination of Environmental Noise LevelsrdquoInternational Standard ISO 1996ndash22007 International Organi-zation for Standardization (2007)

2 ldquoSeattle Municipal Code Chapter 2508 Noise Controlrdquo Seattle(2007)

3 ldquoNoise Control Ordinance of the County of Los Angelesrdquo LosAngeles County (1978)

4 ldquoOrdinance of Minnesota Chapter 389 ndash Noiserdquo Minnesota(2008)

5 NY ADC LAW 27-770 NY code ndash Section 27-770 ldquoNoiseControl of Mechanical Equipmentrdquo New York (2006)

6 RP Hellman ldquoGrowth rate of loudness annoyance and noisi-ness as a function of tone location within the noise spectrumrdquoJ Acoust Soc Am 75(1) 209ndash218 (1984)

7 RP Hellman ldquoPerceived magnitude of two-tone-noise com-plexes loudness annoyance and noisinessrdquo J Acoust SocAm 77(4) 1497ndash1504 (1985)

8 S More and P Davies ldquoHuman responses to the tonalness of air-craft noiserdquo Noise Control Engr J 58(4) 420ndash440 (2010)

9 KH Lee P Davies and AM Surprenant ldquoQuantification ofthe tonal prominence of complex tones in machinery noiserdquoNoiseCon04 (2004)

Fig 9mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals plotted against theproposed linear regression model ofannoyance perception (dashed) basedon ANSI loudness level and tonalaudibility (R2 = 096)

81Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

10 A Hastings H Lee P Davies and AM Surprenant ldquoMeasure-ment of the attributes of complex tonal components commonlyfound in product soundrdquo Noise Control Engr J 51(4) 195ndash209(2003)

11 EE Ryherd and LM Wang ldquoImplications of human perfor-mance and perception under tonal noise conditions on indoornoise criteriardquo J Acoust Soc Am 124(1) 218ndash226 (2008)

12 P Susini S McAdams S Winsberg I Perry S Vieillard andX Rodet ldquoCharacterizing the sound quality of air-conditioningnoiserdquo Appl Acoust 65(8) 763ndash790 (2004)

13 B Berglund P Hassmeacuten and A Preis ldquoAnnoyance and spectralcontrast are cues for similarity and preference of soundsrdquo J SoundVibr 250(1) 53ndash64 (2002)

14 U Landstroumlm E Aringkerlund A Kjellberg and M Tesarz ldquoEx-posure levels tonal components and noise annoyance in work-ing environmentsrdquo Environ Int 21(3) 265ndash275 (1995)

15 U Landstroumlm A Kjellberg and M Bystroumlm ldquoAcceptablelevels of sounds with different spectral characteristics duringthe performance of a simple and a complex non-auditory taskrdquoJ Sound Vibr 160(3) 533ndash542 (1993)

16 D Laird ldquoThe influence of noise on production and fatigue asrelated to pitch sensation level and steadiness of the noiserdquo JAppl Psychol 17(3) 320ndash330 (1933)

17 JV Grjmaldi ldquoSensori-motor performance under varying noiseconditionsrdquo Ergonomics 2(1) 34ndash43 (1958)

18 K Holmberg U Landstrom and A Kjellberg ldquoEffects of ven-tilation noise due to frequency characteristic and sound levelrdquoJ Low Freq Noise Vib 12(4) 115ndash122 (1993)

19 International Organization for Standardization (ISO) ldquoAcousticsmdashAssessment of noise annoyance by means of social and socio-acoustic surveysrdquo International Standard ISOTS 15666 2003International Organization for Standardization (2003)

20 TH Pedersen ldquoThe ldquoGenlydrdquo noise annoyance model dose-response relationships modelled by logistic functionsrdquo DELTAHoslashrsholm Danmark (2007)

21 CMarquis-Favre E Premat andDAubreacutee ldquoNoise and its effectsmdasha review on qualitative aspects of sound Part II noise and annoy-ancerdquo Acta Acust united with Acust 91(4) 626ndash642 (2005)

22 American National Standards Institute (ANSI) ldquoAcousticsmdashMeasurement of Airborne Noise Emitted by Information Technol-ogy and Telecommunications EquipmentmdashPart 1 Determinationof Sound Power Level and Emission Sound Pressure LevelrdquoAmerican National Standards Institute ANSIASA S1210-2010Part 1 American Society of America (2010)

23 W Aures ldquoThe sensory euphony as a function of auditory sen-sationsrdquo Acustica 58(5) 282ndash290 (1985)

24 American National Standards Institute (ANSI) ldquoProcedure forthe Computation of Loudness of Steady Soundsrdquo AmericanNational Standards Institute ANSI S34-2007 Acoustical Soci-ety of America (2007)

25 ldquoAcousticsmdashMethod for Calculating Loudness Levelrdquo Interna-tional Standard ISO 5321975 International Organization forStandardization (1975)

26 E Zwicker ldquoSubdivision of the audible frequency range intocritical bands (Frequenzgruppen)rdquo J Acoust Soc Am 33(2)248ndash248 (1961)

27 BR Glasberg and BCJ Moore ldquoPrediction of absolutethresholds and equal loudness contours using a modified loud-ness modelrdquo J Acoust Soc Am 120 585ndash588 (2006)

28 Federal Aviation Administration ldquoPart 36 Noise StandardsAircraft Type and Airworthiness Certificationrdquo Federal Avia-tion Regulations Federal Aviation Administration (1969)

29 Air-Conditioning Heating and Refrigeration Institute (AHRI)ldquoSound Quality Evaluation Procedures for Air-Conditioningand Refrigeration Equipmentrdquo Air-Conditioning Heating andRefrigeration Institute AHRIANSI 1140-2012 Air-ConditioningHeating and Refrigeration Institute (2012)

30 WE Blazier ldquoRevised noise criteria for application in theacoustical design and rating of HVAC systemsrdquo Noise ControlEngr 16(2) 64ndash73 (1981)

31 F Faul E Erdfelder A Buchner and A-G Lang ldquoStatisticalpower analyses using GPower 31 tests for correlation and re-gression analysesrdquo Behav Res Methods 41 1149ndash1160 (2009)

32 M Schutte A Marks E Wenning and B Griefahn ldquoThe de-velopment of the noise sensitivity questionnairerdquo Noise Heal9(34) 15 (2007)

33 J Francis J Lee A Steinbach and LM Wang ldquoDeterminingannoyance thresholds of tones in noiserdquo ASHRAE Trans120 (2014)

34 JW Grabbe ldquoSudoku and working memory performance forolder adultsrdquo Act Adapt Aging 35(3) 241ndash254 (2011)

35 HS Chang and JM Gibson ldquoThe oddndasheven effect in Sudokupuzzles effects of working memory aging and experiencerdquoAm J Psychol 124(3) 313ndash324 (2011)

36 ldquoNASA TLX Task Load Indexrdquo [Online] Available at httphumansystemsarcnasagovgroupstlx (accessed September 92015)

37 RFS Job J Hatfield NL Carter P Peploe R Taylor andS Morrell ldquoGeneral scales of community reaction to noise(dissatisfaction and perceived affectedness) are more reliablethan scales of annoyancerdquo J Acoust Soc Am 110(2) 939ndash946(2001)

82 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

metrics tone-to-noise ratio and prominence ratio Onemajor difference is that tonal audibility uses A-weightedsound pressure levels and includes a frequency correctionterm in its calculation so that the prominence criteria oftones is constant across frequencies whereas TNR andPR ratings are based on unweighted sound pressure levelsConsequently the prominence of tones is frequency de-pendent for TNR and PR ratings but not for ΔLta Thatis PR = 5 for a 100Hz tone is not necessarily the same per-ceived tonality as PR = 5 for a 500 Hz tone The other dif-ference is that the tonal audibility uses a linear regressionline instead of actual noise components when calculatingmasking tonal levels within the critical bands The equa-tion to calculate ΔLta is given by

ΔLta frac14 Lpt Lpn thorn 2 dBthorn log 1thorn fc502

25

eth1THORN

where Lpt is the total sound pressure level of the tones Lpnis the total sound pressure level of the masking noise in thecritical band and fc is the center frequency of the criticalband Based on the tonal audibility calculation penaltyfactors between 0 and 6 dB are provided to adjust the over-all A-weighted noise levels rather than setting prominencecriteria It also requires separate analysis for each tonewithin a multi-tonal noise signal Aures tonality (Aures)is another metric for tonality that considers the frequencyas well as bandwidth and levels of all tonal componentsthrough use of weighting functions23 It is one of the fewthat can account for multiple tones in a signal

Popular loudness metrics are also investigated in thisstudy because previous studies have found that loudnessof the noise is the most relevant feature correlating to an-noyance besides tonality Among the included loudnessmetrics are A-weighted (dBA) and unweighted (dB)equivalent sound pressure levels and stationary loudnesslevels calculated according to ANSI S34-200724 (ANSIloudness) and ISO 532-1975 B method25 (ISO loudness)The ISO loudness and ANSI loudness are based onZwickers26 and Glasberg and Moores27 loudness mod-els respectively They both can use stationary one-thirdoctave band data for the calculation of loudness

A few noise metrics that consider both loudness andtonality to produce an overall rating for tonal noiseshave been proposed These combined metrics basicallyadd penalty values to the loudness levels due to thepresence of tones The Joint Nordic Method (JNM) isstandardized in ISO 1996-220071 where the penaltyk values are derived from tonal audibility and addedto A-weighted sound pressure level Perceived noiselevel (PNL) was implemented to quantify subjective an-noyance of aircraft noise calculated from one-third oc-tave band values tone-corrected perceived noise level

(PNLT) is a revised version of PNL with the additionof a tone correction factor28 Sound quality indicator (SQI)is a similar metric suggested by the Air-ConditioningHeating and Refrigeration Institute to rate the sound qual-ity of building mechanical product noise based on one-third octave bands29 but it has yet to be applied widely

2 METHODOLOGY

21 Test Laboratory

The subjective testing was completed in an acoustictesting chamber at the University of Nebraska Figure 1illustrates a schematic plan of the testing chamber whichhas a volume of approximately 278 m3 The chamber isacoustically isolated from a monitor room and nearbyspaces Materials in the room include carpeted floorgypsum board walls with additional absorptive panelsacoustic bass traps and acoustical ceiling tiles The av-erage mid-frequency reverberation time is 031 secondsand the ambient background noise level is 37 dBAwhenair-conditioning in the chamber is turned off Figure 2presents the ambient background noise levels in thechamber across octave bands The tonal test signals weregenerated through a ceiling-mounted Armstrong i-ceilingspeaker and a sub-woofer in a corner The i-ceiling speakerappears as other ceiling tiles in the ceiling grid so thatparticipants cannot visually identify the location of thesound source Participants sat in the middle of the cham-ber and were advised not to move their location duringthe experiment

22 Test Signals

A total of 22 noise signals were generated for use inthis study by the program Test Tone Generator fromEsser Audio Two levels of broadband noise without any

Fig 1mdashSchematic plan of the acoustic testingchamber at the University of Nebraska

73Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

tonal components were used either 40 or 55 dBA overallfollowing a 5 dBoctave room criteria (RC) contour30These two levels are in the range of common backgroundnoise levels found in buildings A single tone at one oftwo frequencies and at one of five prominence levelswas added separately to the broadband noise signalsto create the other 20 noise signals The two tonal fre-quencies were selected to be 125 Hz which is a com-mon tone generated by building mechanical equipmentand 500 Hz as it is slightly higher but still in the fre-quency range where a number of other building mechan-ical equipment exhibit tones The five tone levels wereselected to range from below to above the prominencethresholds listed in ANSI S1210-201022 PR = 18 dBfor 125 Hz and PR = 12 dB for 500 Hz Table 1 pre-sents the prominence ratio values for each test signalFigure 3 illustrates the one-third octave band spectra ofthe test signals All tonal signals were measured using a BampK 4189-A microphone through the BampK PULSE sys-

tem at the listeners ear position in the testing chamberand averaged over a minute for calculation of noise metricsThe metrics were calculated using Matlab or programs pro-vided by the associated standards

23 Test Participants and Procedure

Ten participants four females and six males wererecruited from the University of Nebraska mdash Omahacommunity ranging in age from 25 to 43 years oldThe University of Nebraska mdash Lincoln InstitutionalReview Board approved the study and each participantwas paid for their time The sample size was determinedby a priori power analysis using the effect size from Moreand Davies8 statistical results using GPower version3131 The effect size for multiple regression modelsCohens f 2 was calculated as 669 from the squared mul-tiple correlation values in the previous study The minimum

Fig 2mdashMeasured octave band spectra for theambient background noise in the testchamber when air-conditioning is off

Table 1mdashProminence ratios for the tones in the noisestimuli used in the subjective testing aslisted by tonal frequency broadband back-ground noise level and tone level

Frequency(Hz)

BNL(dBA)

Prominence ratio (dB)

Tonelevel1

Tonelevel2

Tonelevel3

Tonelevel4

Tonelevel5

125 40 15 18 21 24 2755 13 15 18 21 24

500 40 9 12 15 18 2155 6 9 12 15 18

Fig 3mdashMeasured one-third octave bandspectra for a few of the test noisesignals (a) Broadband 40 dBA signaland some with assorted tones(b) Broadband 55 dBA signal andsome with assorted tones Tones wereeither at 125 or 500 Hz for clarity onlythe lowest and highest tonal strengthsare presented

74 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

sample size was then found to be six participants to achieve80 power (1 b) at two-sided 5 significance level (a)Based on this finding and available research funds a test-ing plan was designed to assess 22 signals across tentest subjects

All participants completed an orientation session includ-ing a hearing screening test before participation and dem-onstrated normal hearing with thresholds below 25 dBhearing level (HL) from 125 Hz to 8 kHz The noisesensitivity of each participant was also measured by areduced version (13 items only) of the Noise-Sensitivity-Questionnaire (NoiSeQ) by Schutte et al32 during the ori-entation session The participants were asked to answereach item using a four-level rating scale (strongly agree = 1slightly agree = 2 slightly disagree = 3 strongly dis-agree = 4) The responses were averaged across all itemsto form a composite scale to quantify the noise sensitivityfor each participant

The main test consisted of two parts a direct assess-ment with task (part A) and a magnitude adjustment test(part B) The results of part B have been presented inanother paper33 and hence are not included herein In partA participants were asked to complete as many Sudokunumber puzzles as possible while exposed to a broad-band noise signal some with assorted tonal compo-nents for 10 minutes Sudoku puzzles were selected asthe measure of task performance as they are compact toadminister easy to explain to test participants and havebeen used as a measure of task performance in otherstudies with results showing significant relationship withworking memory3435 All participants practiced solvingSudoku puzzles during the orientation session before par-ticipating in the main test and the difficulty of all Sudokupuzzles in the main test was held constant The puzzleswere all nine by nine with forty of the eighty-one grids be-ing prefilled with numbers

After spending 10 minutes solving the Sudoku puz-zles the subjects answered five questions on a subjec-tive questionnaire about the noise they had just heardThe questionnaire was a modified version of the NASAtask load index36 The original NASA task load index isdivided into six subscales mental demand physical de-mand temporal demand performance effort and frus-tration In this study the questions on physical demandtemporal demand and frustration were not included in-stead questions were added on rating loudness and annoy-ance incurred by noise as shown in Table 2 Participantsresponded to each question based on a 21-point scale (tomatch the scale from the original NASA task load index)on a paper form

Part A consisted of ten 30-minute sessions that werecompleted by each subject individually on differentdays Within each 30-minute session subjects were ex-posed to three noise signals (each for 10 minutes) and

thus completed three sequences of Sudoku puzzles (dif-ferent puzzles each time) followed by the questionnaireTo minimize the influence of back-to-back comparisonsof tonal noise conditions a neutral background noisecondition without any tonal components was used asthe second signal within each 30-minute test sessionWithin a single 30-minute test session the noise levelof the broadband noise without consideration for anytonal components remained at a constant level either40 or 55 dBA The presentation order of the backgroundnoise levels and tonal test signals was carefully balancedacross all subjects using a Latin square design

Two task performance measures were gathered by(1) counting the amount of Sudoku puzzles a subjectcompleted within a 10-minute trial with partial comple-tions included as well and (2) quantifying the accuracyof the puzzle answers in terms of correct numbers amongthose answered in a puzzle The maximum and mini-mum number of Sudoku puzzles participants completedin one 10-minute session were 28 and 03 and the max-imum and minimum accuracy of the puzzles were 100and 69

3 RESULTS AND DISCUSSION

The reliability of each participants responses wasdetermined from correlation analysis of the participantsindividual annoyance responses to a loudness metrictonality metric and average ratings across participants8Figure 4 presents correlation coefficients of each parti-cipants annoyance responses to the ANSI loudnesslevel tonal audibility and mean values across partici-pants Two participants responses (number 6 and 8)were excluded from all analyses because they ratedresponses randomly regardless of sound characteristics(correlation value lt02) The subject-to-loudness co-efficient of participant 6 was 017 and the subject-to-

Table 2mdashItems from the subjective questionnaireas modified from the NASA task loadindex

Description Questions

Mental demand 1 How mentally demandingwas the task

Overall performance 2 How successful wereyou in accomplishing whatyou were asked to do

Effort 3 How hard did you haveto work to accomplish yourlevel of performance

Loudness 4 How loud was the noiseAnnoyance 5 How annoying was the noise

75Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

tonality correlation coefficient of participant 8 was 003All subsequent analyses are based on the remaining eightsubjects which is still above the minimum sample size ofsix recommended from the a priori power analysis Addi-tionally outlier responses of annoyance and task perfor-mance scores were excluded from the statistical analysispresented below The outliers were identified using the cri-terion of being beyond three standard deviations from theaverage across participants Two outliers of annoyance rat-ings and three outliers of task performance scores wereexcluded for analyses based on this criterion

31 Relating Task Performance to SubjectiveResponses and to Noise Attributes

The task performance measures related to the Sudokupuzzles were correlated to the participants subjective

responses on the modified NASA task load index ques-tionnaires (Table 3) Spearmans correlation (r) was utilizedbecause not all of the variables met the assumption ofhaving a normal distribution with the sample size uti-lized An additional ldquoTLX-avgrdquo score was calculated asthe averaged value of all five items from the modified sur-vey to represent an overall rating of subjective task loadperception induced by noise exposure Since the task diffi-culty was held constant with equivalently difficult Sudokupuzzles throughout the experiment the variations in sub-jective ratings observed within subjects can be consideredas the result of varying background noise conditions Jobet al37 have recommended against using a single questionitem about annoyance because of its reduced validity con-sequently the composite modified Noise TLX rating isproposed as an alternative in this laboratory study Witha Cronbachs a coefficient for the reliability of 082 anda testndashretest correlation of the Noise TLX measure forthe stability of 077 the ldquoTLX-avgrdquo questionnaire wasfound to be internally consistent and stable over timeand thus suitable for the purpose of this test

As Table 3 indicates most of the subjective responseswere significantly correlated with each other Specificallyof interest the mental demand responses showed highcorrelations with perceptions of loudness and annoy-ance of the noise and as expected loudness and annoy-ance ratings were significantly correlated with each other(r = 0948) The only statistically significant correla-tion between a task performance result and a subjec-tive response was between ldquoaccuracyrdquo (accuracy ratesof participants puzzle answers) and responses to theldquoperformancerdquo question on the questionnaire (r =0483)

Figures 5 and 6 present the averaged task perfor-mance of the accuracy and number of completed puzzles

Fig 4mdashCorrelation coefficients of eachparticipants annoyance responses toeach signals ANSI loudness tonalaudibility and group average

Table 3mdashSpearmans correlation analysis of the subjective responses and Sudoku puzzle task performancemeasures TLX-avg is the average value of the responses to all five questions on the modified taskload index questionnaire ldquoNo of completedrdquo refers to the number of completed puzzles for eachtrial and ldquoaccuracyrdquo indicates accuracy rates of participants puzzle answers

Mentaldemand

Performance Effort Loudness Annoyance TLX-avg

No ofcompleted

Accuracy

Mental demand ndashPerformance 0260 ndashEffort 0610 0496 ndashLoudness 0501 0105 0230 ndashAnnoyance 0528 0162 0398 0948 ndashTLXndashavg 0631 0374 0601 0880 0956 ndashNo of puzzles completed 0317 0438 0394 0074 0020 0171 ndashAccuracy 0105 0483 0071 0289 0252 0330 0080 ndash

Correlation is significant at the 001 level (2-tailed)Correlation is significant at the 005 level (2-tailed)

76 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

against the physical attributes of the noise signals in-cluding background noise level tone frequency andthe five levels of tone strengths The repeated measureANOVA (analysis of variance) confirms that there wereno statistically significant differences between task per-formances across the various noise attributes Thussubjects did not complete more puzzles or have higheraccuracy under any particular tonal frequency back-ground noise level or tone strength although there

appears to be a slight tendency of lower accuracy withgreater tone strength

32 Relating Noise Attributes toAnnoyance Responses

To understand how the physical aspects of the noisesignals (background noise level tone frequency andtonal strength) related to annoyance a three-way repeated

Fig 5mdashAveraged accuracy of Sudoku puzzle answers as task performance scores plotted against(a) Background noise level (b) Tonal frequency and (c) Strength of the tones where Tone1 indicates the least prominent tone and Tone 5 indicates the most prominent tone Errorbars indicate one standard error

Fig 6mdashAveraged number of completed Sudoku puzzles as task performance scores plotted against(a) Background noise level (b) Tonal frequency and (c) Strength of the tones where Tone1 indicates the least prominent tone and Tone 5 indicates the most prominent tone Errorbars indicate one standard error

77Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

measure ANOVAwas conducted Mauchlys test indicatedthat the assumption of sphericity had been met for themain effects of tonal strength and its interactions withtone frequency and background noise level The analysisshows a significant main effect of background noise level[F(17) = 8261 p lt 0001 2p = 092] tone frequency

[F(17) = 2001 p = 0003 2p = 074] and tonal strength

[F(428) = 476 p = 0005 2p = 041] on annoyanceThe main analysis shows that the 55 dBA based

tonal signals were significantly more annoying than40 dBA based tonal signals and that the 125 Hz tonalsignals were significantly more annoying than 500 Hztonal signals Contrast comparisons reveal that the 4thhighest [F(17) = 10420 p = 0014] and 5th highest[F(17) = 12069 p = 0010] in prominence tonal signalswere perceived as more annoying than the least (1st)prominent tonal signals

Figure 7 illustrates the mean annoyance ratings acrossbackground noise levels tonal frequencies and tonestrengths Summarizing these results the overall back-ground noise level does impact annoyance with higherlevels leading to greater annoyance The lower fre-quency tone generated greater annoyance ratings butone should note that the prominence levels of the125 Hz tone versus those of the 500 Hz tone used inthe study were not the same even though the relativedifferences from the threshold of tones presented inISO 1996-22007 are the same There was also a sig-nificant interaction effect between background noiselevel and tone frequency [F(17) = 3331 p = 0014

2p = 060] As plotted in Fig 7(d) the difference be-tween annoyance ratings of the 125 and 500 Hz toneswas greater with the 40 dBA background noise levelcondition than with the 55 dBA background noiselevel condition It appears that tonal frequency is lessrelated to annoyance at higher background noiselevels but plays a larger role at lower backgroundnoise levels

The data on tonal strength shows that higher tone levelsare linked to higher annoyance ratings analysis of the datato determine a threshold of annoyance is presented inFrancis et als study33 Noise sensitivity was expected tobe associated with annoyance but did not demonstrate sta-tistically significant effects in the ANOVA analysis as abetween-subjects factor This is attributed to the limitednumber of subjects in the study which was selected basedon a power analysis of previous annoyance results ratherthan noise sensitivity results

33 Correlations of Noise Metrics withSubjective Responses

The previous section showed that physical aspects ofthe noise signals (specifically loudness and tonality) werecorrelated with annoyance responses in this sectionassorted metrics for quantifying those physical aspectsare tested against the subjective responses Spearmansnonparametric correlation coefficients were calculatedbetween a number of noise metrics and the average par-ticipants perception ratings of loudness annoyance andTLX-avg The results have been analyzed in two ways

Fig 7mdashMean annoyance perception ratings plotted against (a) Background noise level (b) Tonalfrequency (c) Strength of the tones where Tone 1 indicates the least prominent tone andTone 5 indicates the most prominent tone and (d) Interaction of background noise leveland tonal frequency Error bars indicate one standard error

78 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

first with all twenty tonal signals included and then withthe average ratings for ten signals grouped separately bythe broadband background noise level (40 or 55 dBA)Table 4 presents correlation coefficients between all noisemetrics with the subjective perception responses

When analyzing all signals the noise metric thatdemonstrates the highest correlation coefficients withthe perceived loudness annoyance and TLX-avg rat-ings is ANSI loudness level Other loudness metricswere also significantly correlated to the perception rat-ings but the tonality metrics such as prominence ratiotone-to-noise ratio tonality audibility and Aures tonal-ity did not statistically correlate or had lower coefficientsthan loudness metrics This confirms that loudness is themost dominant factor in determining subjective percep-tion of noise

When the signals are grouped separately by broadbandbackground noise levels though tonality metrics didshow higher correlations with subjective ratings thanloudness metrics The coefficient values for the assortedtonality metrics are all very similar with no particularmetric clearly performing better than others Howeverwhen only looking for correlation coefficients with an-noyance tonal audibility showed slightly higher corre-lation coefficients than other tonality metrics (0888for 40 dBA BNL and 0891 for 55 dBA BNL) Aurestonality also showed high correlation with annoyancefrom 55 dBA BNL signals (0903) but it showed lowercorrelation than other metrics with 40 dBA BNL sig-nals (0709) The results indicate that when the broad-band background noise level is controlled or comparabletonality becomes a more influencing factor on annoyanceevaluation Figure 8 presents scatterplots of the averagedannoyance responses (a) with the ANSI loudness levelacross the entire group and (b) with tonal audibility sepa-rated by background noise level

For all cases combined metrics such as the JointNordic Method tone-corrected perceived noise leveland sound quality indicator did not show remarkablybetter performance than loudness metrics even thoughthese combined metrics were significantly related withannoyance ratings The results suggest that imposingpenalty values to loudness levels based on tonal strengthmay not be the most appropriate way to quantify overallsubjective annoyance of tonal noise Instead using sepa-rate metrics to account for tonality and loudness of build-ing mechanical noises is an effective way to relate to thesignals annoyance

34 Regression Model between Noise Metricsand Annoyance

Based on the results in Table 4 ANSI loudness leveland tonal audibility were selected to be used as predictors

Table 4mdashSpearmans correlation analysis of noisemetrics against subjective responses andSudoku puzzle task performance The resultsare analyzed first with all signals includedand then in two groups separated by back-ground noise level (40 or 55 dBA) Boldedvalues indicate metrics chosen for use inthe regression model based on their overallhigh significant correlation values

All signals (40 dBA and 55 dBA BNL)

Loudness Annoyance TLX-avgPR 0150 0186 0147TNR 0123 0081 0095ΔLta 0006 0056 0019Aures 0297 0359 0314dB 0805 0824 0772dBA 0866 0887 0842ANSI loudness 0946 0950 0926ISO loudness 0938 0952 0925PNL 0892 0920 0886PNLT 0869 0877 0826JNM 0840 0869 0818SQI 0904 0899 0856

40 dBA BNL only

PR 0794 0867 0782TNR 0794 0867 0782ΔLta 0778 0888 0815Aures 0673 0709 0697dB 0806 0939 0855dBA 0794 0927 0830ANSI loudness 0685 0745 0697ISO loudness 0685 0745 0697PNL 0685 0842 0867PNLT 0794 0830 0758JNM 0794 0927 0830SQI 0806 0806 0709

55 dBA BNL only

PR 0799 0867 0758TNR 0709 0845 0845ΔLta 0787 0891 0818Aures 0781 0903 0782dB 0715 0756 0530dBA 0707 0770 0564ANSI loudness 0878 0855 0709ISO loudness 0817 0867 0697PNL 0720 0806 0539PNLT 0744 0782 0527JNM 0707 0770 0564SQI 0689 0663 0444

Correlation is significant at the 001 level (2-tailed)Correlation is significant at the 005 level (2-tailed)

79Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

for a linear multiple regression model for annoyancebecause these two metrics resulted in among the stron-gest correlations with annoyance perception compared

to other noise metrics Equation (2) presents the multi-variate regression model with ANSI loudness level andtonal audibility

Annoyance frac14 1806 thorn 1164 ANSI Loudness soneeth THORNfrac12 thorn 0072 Tonal Audibility dBeth THORNfrac12

eth2THORN

Table 5 also presents standard error of coefficientsstandardized coefficients and statistical significance whenANSI loudness level was only used (in step 1) and whentonal audibility was also included (in step 2) in additionto the coefficient values for each predictor Standardizedb values indicate the number of standard deviations thatthe outcome annoyance will change as a result of onestandard deviation change in the predictor The R2 valuefor the first step model is 0943 which is a measure ofgoodness-of-fit of linear regression indicating that 943of the annoyance rating variance can be explained by theANSI loudness model only When including tonal audibil-ity as a second predictor the R2 value increased to 0962Even though this increase is small the multivariate re-gression model does significantly predict more variationin annoyance perception when including tonal audibilityas a second predictor for step 2 the ANSI loudnesslevel [t(17) = 20796 p lt 0001] and tonal audibility[t(17) = 2943 p = 0009] are both significant predictorsof annoyance Figure 9 illustrates a regression line withthe calculated linear model

The results of the correlation analysis and regressionmodel presented in this paper are in line with the find-ings from More and Daviesrsquo study8 which focused onaircraft flyover noise rather than building mechanicalsystem noise Their work focused only on annoyanceand used metric values that were exceeded some per-centage (often 5) of the time since their flyover

Fig 8mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals against (a) ANSIloudness level for all signals and(b) Tonal audibility for 40 and 55 dBABNL separately Dashed lines indicateregression lines of annoyance ratingprediction with regard to each metric

Table 5mdashLinear regression model of predictors for annoyance perception with 95 bias corrected and accel-erated confidence intervals reported in parentheses Confidence intervals and standard errors arebased on 1000 bootstrap samples Standardized b values indicate the number of standard deviationsthat the outcome annoyance will change as a result of one standard deviation change in the predictor

b Standarderror B

b p

Step 1Constant 3254(2305 4310) 0512 p = 0001ANSI loudness (sone) 1137(1004 1263) 0066 0971 p = 0001

Step 2Constant 1806(0498 3187) 0683 p = 0020ANSI loudness (sone) 1164(1043 1308) 0069 0994 p = 0001Tonal audibility (dB) 0072(0027 0111) 0021 0141 p = 0004

Note 0943 for Step 1 ΔR2 = 0019 for Step 2

80 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

signals varied in time They demonstrated that the bestregression model when using existing metrics to matchtheir annoyance responses included both a loudnessmetric and a tonality metric and they proposed revi-sion to the penalty values used for the Joint NordicMethod to improve its individual predictive capabilityThe authors feel though that it is not necessary tomodify a combined metric (like Joint Nordic Method)since using individual metrics for loudness and for to-nality in the proposed regression model herein demon-strated high correlations to the annoyance responseson their own

4 SUMMARYAND CONCLUSION

The purpose of this study was to investigate hownoise signals with varying degrees of prominent tonessimilar to those produced by building mechanicalequipment affect subjective annoyance perception andtask performance and to develop a prediction modelof annoyance using current noise metrics Subjectscompleted Sudoku puzzles and a questionnaire modi-fied from the NASA task load index to quantify theoverall workload caused by building mechanical noisein this study No statistically significant effect was foundbetween the tonal signals used in this study and task per-formance although there was a trend of decreasing accu-racy with increasing tone strengths based on correctSudoku puzzle answers The validity of the modified taskload index questionnaire was high based on its reliabilitycoefficient and testndashretest coefficient and the average re-sponse from the questionnaire was found to significantlycorrelate with perceived annoyance and loudness of thebackground noise signals A factorial repeated measure

ANOVA revealed that participants felt more annoyed withincreasing background noise level lower tone frequencyand higher tone strength Correlation analysis with noisemetrics and subjective perception ratings found that ANSIloudness level among all other loudness metrics correlatesmost strongly with annoyance perception while assortedtonality metrics showed relatively weaker but still statisti-cally significant correlations with annoyance A statisticallysignificant multivariate regression model with ANSI loud-ness level and tonal audibility has been developed whichdemonstrates an R2 value of 0962

While noise sensitivity of test subjects was surveyedno statistically significant relations between perceptionor performance results and noise sensitivity were foundlikely due to the limited number of test subjects Futurework in this area is suggested with more test subjectsand more tonal signals to understand better the roleof noise sensitivity Also tonal noises from actual buildingmechanical systems often demonstrate multiple tones whichmay be inharmonic or which can fluctuate in time addi-tional investigations using tonal signals that incorporatethese other factors are recommended

5 ACKNOWLEDGMENTS

Many thanks to the undergraduate research assis-tants Adam Steinbach and Kristin Hanna who assistedwith the study design subjective testing and data analysisPartial funding for this project came from an Institute ofNoise Control Engineering Undergraduate Research Grant

6 REFERENCES

1 International Organization for Standardization (ISO) ldquoAcousticsmdashDescription Measurement and Assessment of EnvironmentalNoisemdashPart 2 Determination of Environmental Noise LevelsrdquoInternational Standard ISO 1996ndash22007 International Organi-zation for Standardization (2007)

2 ldquoSeattle Municipal Code Chapter 2508 Noise Controlrdquo Seattle(2007)

3 ldquoNoise Control Ordinance of the County of Los Angelesrdquo LosAngeles County (1978)

4 ldquoOrdinance of Minnesota Chapter 389 ndash Noiserdquo Minnesota(2008)

5 NY ADC LAW 27-770 NY code ndash Section 27-770 ldquoNoiseControl of Mechanical Equipmentrdquo New York (2006)

6 RP Hellman ldquoGrowth rate of loudness annoyance and noisi-ness as a function of tone location within the noise spectrumrdquoJ Acoust Soc Am 75(1) 209ndash218 (1984)

7 RP Hellman ldquoPerceived magnitude of two-tone-noise com-plexes loudness annoyance and noisinessrdquo J Acoust SocAm 77(4) 1497ndash1504 (1985)

8 S More and P Davies ldquoHuman responses to the tonalness of air-craft noiserdquo Noise Control Engr J 58(4) 420ndash440 (2010)

9 KH Lee P Davies and AM Surprenant ldquoQuantification ofthe tonal prominence of complex tones in machinery noiserdquoNoiseCon04 (2004)

Fig 9mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals plotted against theproposed linear regression model ofannoyance perception (dashed) basedon ANSI loudness level and tonalaudibility (R2 = 096)

81Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

10 A Hastings H Lee P Davies and AM Surprenant ldquoMeasure-ment of the attributes of complex tonal components commonlyfound in product soundrdquo Noise Control Engr J 51(4) 195ndash209(2003)

11 EE Ryherd and LM Wang ldquoImplications of human perfor-mance and perception under tonal noise conditions on indoornoise criteriardquo J Acoust Soc Am 124(1) 218ndash226 (2008)

12 P Susini S McAdams S Winsberg I Perry S Vieillard andX Rodet ldquoCharacterizing the sound quality of air-conditioningnoiserdquo Appl Acoust 65(8) 763ndash790 (2004)

13 B Berglund P Hassmeacuten and A Preis ldquoAnnoyance and spectralcontrast are cues for similarity and preference of soundsrdquo J SoundVibr 250(1) 53ndash64 (2002)

14 U Landstroumlm E Aringkerlund A Kjellberg and M Tesarz ldquoEx-posure levels tonal components and noise annoyance in work-ing environmentsrdquo Environ Int 21(3) 265ndash275 (1995)

15 U Landstroumlm A Kjellberg and M Bystroumlm ldquoAcceptablelevels of sounds with different spectral characteristics duringthe performance of a simple and a complex non-auditory taskrdquoJ Sound Vibr 160(3) 533ndash542 (1993)

16 D Laird ldquoThe influence of noise on production and fatigue asrelated to pitch sensation level and steadiness of the noiserdquo JAppl Psychol 17(3) 320ndash330 (1933)

17 JV Grjmaldi ldquoSensori-motor performance under varying noiseconditionsrdquo Ergonomics 2(1) 34ndash43 (1958)

18 K Holmberg U Landstrom and A Kjellberg ldquoEffects of ven-tilation noise due to frequency characteristic and sound levelrdquoJ Low Freq Noise Vib 12(4) 115ndash122 (1993)

19 International Organization for Standardization (ISO) ldquoAcousticsmdashAssessment of noise annoyance by means of social and socio-acoustic surveysrdquo International Standard ISOTS 15666 2003International Organization for Standardization (2003)

20 TH Pedersen ldquoThe ldquoGenlydrdquo noise annoyance model dose-response relationships modelled by logistic functionsrdquo DELTAHoslashrsholm Danmark (2007)

21 CMarquis-Favre E Premat andDAubreacutee ldquoNoise and its effectsmdasha review on qualitative aspects of sound Part II noise and annoy-ancerdquo Acta Acust united with Acust 91(4) 626ndash642 (2005)

22 American National Standards Institute (ANSI) ldquoAcousticsmdashMeasurement of Airborne Noise Emitted by Information Technol-ogy and Telecommunications EquipmentmdashPart 1 Determinationof Sound Power Level and Emission Sound Pressure LevelrdquoAmerican National Standards Institute ANSIASA S1210-2010Part 1 American Society of America (2010)

23 W Aures ldquoThe sensory euphony as a function of auditory sen-sationsrdquo Acustica 58(5) 282ndash290 (1985)

24 American National Standards Institute (ANSI) ldquoProcedure forthe Computation of Loudness of Steady Soundsrdquo AmericanNational Standards Institute ANSI S34-2007 Acoustical Soci-ety of America (2007)

25 ldquoAcousticsmdashMethod for Calculating Loudness Levelrdquo Interna-tional Standard ISO 5321975 International Organization forStandardization (1975)

26 E Zwicker ldquoSubdivision of the audible frequency range intocritical bands (Frequenzgruppen)rdquo J Acoust Soc Am 33(2)248ndash248 (1961)

27 BR Glasberg and BCJ Moore ldquoPrediction of absolutethresholds and equal loudness contours using a modified loud-ness modelrdquo J Acoust Soc Am 120 585ndash588 (2006)

28 Federal Aviation Administration ldquoPart 36 Noise StandardsAircraft Type and Airworthiness Certificationrdquo Federal Avia-tion Regulations Federal Aviation Administration (1969)

29 Air-Conditioning Heating and Refrigeration Institute (AHRI)ldquoSound Quality Evaluation Procedures for Air-Conditioningand Refrigeration Equipmentrdquo Air-Conditioning Heating andRefrigeration Institute AHRIANSI 1140-2012 Air-ConditioningHeating and Refrigeration Institute (2012)

30 WE Blazier ldquoRevised noise criteria for application in theacoustical design and rating of HVAC systemsrdquo Noise ControlEngr 16(2) 64ndash73 (1981)

31 F Faul E Erdfelder A Buchner and A-G Lang ldquoStatisticalpower analyses using GPower 31 tests for correlation and re-gression analysesrdquo Behav Res Methods 41 1149ndash1160 (2009)

32 M Schutte A Marks E Wenning and B Griefahn ldquoThe de-velopment of the noise sensitivity questionnairerdquo Noise Heal9(34) 15 (2007)

33 J Francis J Lee A Steinbach and LM Wang ldquoDeterminingannoyance thresholds of tones in noiserdquo ASHRAE Trans120 (2014)

34 JW Grabbe ldquoSudoku and working memory performance forolder adultsrdquo Act Adapt Aging 35(3) 241ndash254 (2011)

35 HS Chang and JM Gibson ldquoThe oddndasheven effect in Sudokupuzzles effects of working memory aging and experiencerdquoAm J Psychol 124(3) 313ndash324 (2011)

36 ldquoNASA TLX Task Load Indexrdquo [Online] Available at httphumansystemsarcnasagovgroupstlx (accessed September 92015)

37 RFS Job J Hatfield NL Carter P Peploe R Taylor andS Morrell ldquoGeneral scales of community reaction to noise(dissatisfaction and perceived affectedness) are more reliablethan scales of annoyancerdquo J Acoust Soc Am 110(2) 939ndash946(2001)

82 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

tonal components were used either 40 or 55 dBA overallfollowing a 5 dBoctave room criteria (RC) contour30These two levels are in the range of common backgroundnoise levels found in buildings A single tone at one oftwo frequencies and at one of five prominence levelswas added separately to the broadband noise signalsto create the other 20 noise signals The two tonal fre-quencies were selected to be 125 Hz which is a com-mon tone generated by building mechanical equipmentand 500 Hz as it is slightly higher but still in the fre-quency range where a number of other building mechan-ical equipment exhibit tones The five tone levels wereselected to range from below to above the prominencethresholds listed in ANSI S1210-201022 PR = 18 dBfor 125 Hz and PR = 12 dB for 500 Hz Table 1 pre-sents the prominence ratio values for each test signalFigure 3 illustrates the one-third octave band spectra ofthe test signals All tonal signals were measured using a BampK 4189-A microphone through the BampK PULSE sys-

tem at the listeners ear position in the testing chamberand averaged over a minute for calculation of noise metricsThe metrics were calculated using Matlab or programs pro-vided by the associated standards

23 Test Participants and Procedure

Ten participants four females and six males wererecruited from the University of Nebraska mdash Omahacommunity ranging in age from 25 to 43 years oldThe University of Nebraska mdash Lincoln InstitutionalReview Board approved the study and each participantwas paid for their time The sample size was determinedby a priori power analysis using the effect size from Moreand Davies8 statistical results using GPower version3131 The effect size for multiple regression modelsCohens f 2 was calculated as 669 from the squared mul-tiple correlation values in the previous study The minimum

Fig 2mdashMeasured octave band spectra for theambient background noise in the testchamber when air-conditioning is off

Table 1mdashProminence ratios for the tones in the noisestimuli used in the subjective testing aslisted by tonal frequency broadband back-ground noise level and tone level

Frequency(Hz)

BNL(dBA)

Prominence ratio (dB)

Tonelevel1

Tonelevel2

Tonelevel3

Tonelevel4

Tonelevel5

125 40 15 18 21 24 2755 13 15 18 21 24

500 40 9 12 15 18 2155 6 9 12 15 18

Fig 3mdashMeasured one-third octave bandspectra for a few of the test noisesignals (a) Broadband 40 dBA signaland some with assorted tones(b) Broadband 55 dBA signal andsome with assorted tones Tones wereeither at 125 or 500 Hz for clarity onlythe lowest and highest tonal strengthsare presented

74 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

sample size was then found to be six participants to achieve80 power (1 b) at two-sided 5 significance level (a)Based on this finding and available research funds a test-ing plan was designed to assess 22 signals across tentest subjects

All participants completed an orientation session includ-ing a hearing screening test before participation and dem-onstrated normal hearing with thresholds below 25 dBhearing level (HL) from 125 Hz to 8 kHz The noisesensitivity of each participant was also measured by areduced version (13 items only) of the Noise-Sensitivity-Questionnaire (NoiSeQ) by Schutte et al32 during the ori-entation session The participants were asked to answereach item using a four-level rating scale (strongly agree = 1slightly agree = 2 slightly disagree = 3 strongly dis-agree = 4) The responses were averaged across all itemsto form a composite scale to quantify the noise sensitivityfor each participant

The main test consisted of two parts a direct assess-ment with task (part A) and a magnitude adjustment test(part B) The results of part B have been presented inanother paper33 and hence are not included herein In partA participants were asked to complete as many Sudokunumber puzzles as possible while exposed to a broad-band noise signal some with assorted tonal compo-nents for 10 minutes Sudoku puzzles were selected asthe measure of task performance as they are compact toadminister easy to explain to test participants and havebeen used as a measure of task performance in otherstudies with results showing significant relationship withworking memory3435 All participants practiced solvingSudoku puzzles during the orientation session before par-ticipating in the main test and the difficulty of all Sudokupuzzles in the main test was held constant The puzzleswere all nine by nine with forty of the eighty-one grids be-ing prefilled with numbers

After spending 10 minutes solving the Sudoku puz-zles the subjects answered five questions on a subjec-tive questionnaire about the noise they had just heardThe questionnaire was a modified version of the NASAtask load index36 The original NASA task load index isdivided into six subscales mental demand physical de-mand temporal demand performance effort and frus-tration In this study the questions on physical demandtemporal demand and frustration were not included in-stead questions were added on rating loudness and annoy-ance incurred by noise as shown in Table 2 Participantsresponded to each question based on a 21-point scale (tomatch the scale from the original NASA task load index)on a paper form

Part A consisted of ten 30-minute sessions that werecompleted by each subject individually on differentdays Within each 30-minute session subjects were ex-posed to three noise signals (each for 10 minutes) and

thus completed three sequences of Sudoku puzzles (dif-ferent puzzles each time) followed by the questionnaireTo minimize the influence of back-to-back comparisonsof tonal noise conditions a neutral background noisecondition without any tonal components was used asthe second signal within each 30-minute test sessionWithin a single 30-minute test session the noise levelof the broadband noise without consideration for anytonal components remained at a constant level either40 or 55 dBA The presentation order of the backgroundnoise levels and tonal test signals was carefully balancedacross all subjects using a Latin square design

Two task performance measures were gathered by(1) counting the amount of Sudoku puzzles a subjectcompleted within a 10-minute trial with partial comple-tions included as well and (2) quantifying the accuracyof the puzzle answers in terms of correct numbers amongthose answered in a puzzle The maximum and mini-mum number of Sudoku puzzles participants completedin one 10-minute session were 28 and 03 and the max-imum and minimum accuracy of the puzzles were 100and 69

3 RESULTS AND DISCUSSION

The reliability of each participants responses wasdetermined from correlation analysis of the participantsindividual annoyance responses to a loudness metrictonality metric and average ratings across participants8Figure 4 presents correlation coefficients of each parti-cipants annoyance responses to the ANSI loudnesslevel tonal audibility and mean values across partici-pants Two participants responses (number 6 and 8)were excluded from all analyses because they ratedresponses randomly regardless of sound characteristics(correlation value lt02) The subject-to-loudness co-efficient of participant 6 was 017 and the subject-to-

Table 2mdashItems from the subjective questionnaireas modified from the NASA task loadindex

Description Questions

Mental demand 1 How mentally demandingwas the task

Overall performance 2 How successful wereyou in accomplishing whatyou were asked to do

Effort 3 How hard did you haveto work to accomplish yourlevel of performance

Loudness 4 How loud was the noiseAnnoyance 5 How annoying was the noise

75Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

tonality correlation coefficient of participant 8 was 003All subsequent analyses are based on the remaining eightsubjects which is still above the minimum sample size ofsix recommended from the a priori power analysis Addi-tionally outlier responses of annoyance and task perfor-mance scores were excluded from the statistical analysispresented below The outliers were identified using the cri-terion of being beyond three standard deviations from theaverage across participants Two outliers of annoyance rat-ings and three outliers of task performance scores wereexcluded for analyses based on this criterion

31 Relating Task Performance to SubjectiveResponses and to Noise Attributes

The task performance measures related to the Sudokupuzzles were correlated to the participants subjective

responses on the modified NASA task load index ques-tionnaires (Table 3) Spearmans correlation (r) was utilizedbecause not all of the variables met the assumption ofhaving a normal distribution with the sample size uti-lized An additional ldquoTLX-avgrdquo score was calculated asthe averaged value of all five items from the modified sur-vey to represent an overall rating of subjective task loadperception induced by noise exposure Since the task diffi-culty was held constant with equivalently difficult Sudokupuzzles throughout the experiment the variations in sub-jective ratings observed within subjects can be consideredas the result of varying background noise conditions Jobet al37 have recommended against using a single questionitem about annoyance because of its reduced validity con-sequently the composite modified Noise TLX rating isproposed as an alternative in this laboratory study Witha Cronbachs a coefficient for the reliability of 082 anda testndashretest correlation of the Noise TLX measure forthe stability of 077 the ldquoTLX-avgrdquo questionnaire wasfound to be internally consistent and stable over timeand thus suitable for the purpose of this test

As Table 3 indicates most of the subjective responseswere significantly correlated with each other Specificallyof interest the mental demand responses showed highcorrelations with perceptions of loudness and annoy-ance of the noise and as expected loudness and annoy-ance ratings were significantly correlated with each other(r = 0948) The only statistically significant correla-tion between a task performance result and a subjec-tive response was between ldquoaccuracyrdquo (accuracy ratesof participants puzzle answers) and responses to theldquoperformancerdquo question on the questionnaire (r =0483)

Figures 5 and 6 present the averaged task perfor-mance of the accuracy and number of completed puzzles

Fig 4mdashCorrelation coefficients of eachparticipants annoyance responses toeach signals ANSI loudness tonalaudibility and group average

Table 3mdashSpearmans correlation analysis of the subjective responses and Sudoku puzzle task performancemeasures TLX-avg is the average value of the responses to all five questions on the modified taskload index questionnaire ldquoNo of completedrdquo refers to the number of completed puzzles for eachtrial and ldquoaccuracyrdquo indicates accuracy rates of participants puzzle answers

Mentaldemand

Performance Effort Loudness Annoyance TLX-avg

No ofcompleted

Accuracy

Mental demand ndashPerformance 0260 ndashEffort 0610 0496 ndashLoudness 0501 0105 0230 ndashAnnoyance 0528 0162 0398 0948 ndashTLXndashavg 0631 0374 0601 0880 0956 ndashNo of puzzles completed 0317 0438 0394 0074 0020 0171 ndashAccuracy 0105 0483 0071 0289 0252 0330 0080 ndash

Correlation is significant at the 001 level (2-tailed)Correlation is significant at the 005 level (2-tailed)

76 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

against the physical attributes of the noise signals in-cluding background noise level tone frequency andthe five levels of tone strengths The repeated measureANOVA (analysis of variance) confirms that there wereno statistically significant differences between task per-formances across the various noise attributes Thussubjects did not complete more puzzles or have higheraccuracy under any particular tonal frequency back-ground noise level or tone strength although there

appears to be a slight tendency of lower accuracy withgreater tone strength

32 Relating Noise Attributes toAnnoyance Responses

To understand how the physical aspects of the noisesignals (background noise level tone frequency andtonal strength) related to annoyance a three-way repeated

Fig 5mdashAveraged accuracy of Sudoku puzzle answers as task performance scores plotted against(a) Background noise level (b) Tonal frequency and (c) Strength of the tones where Tone1 indicates the least prominent tone and Tone 5 indicates the most prominent tone Errorbars indicate one standard error

Fig 6mdashAveraged number of completed Sudoku puzzles as task performance scores plotted against(a) Background noise level (b) Tonal frequency and (c) Strength of the tones where Tone1 indicates the least prominent tone and Tone 5 indicates the most prominent tone Errorbars indicate one standard error

77Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

measure ANOVAwas conducted Mauchlys test indicatedthat the assumption of sphericity had been met for themain effects of tonal strength and its interactions withtone frequency and background noise level The analysisshows a significant main effect of background noise level[F(17) = 8261 p lt 0001 2p = 092] tone frequency

[F(17) = 2001 p = 0003 2p = 074] and tonal strength

[F(428) = 476 p = 0005 2p = 041] on annoyanceThe main analysis shows that the 55 dBA based

tonal signals were significantly more annoying than40 dBA based tonal signals and that the 125 Hz tonalsignals were significantly more annoying than 500 Hztonal signals Contrast comparisons reveal that the 4thhighest [F(17) = 10420 p = 0014] and 5th highest[F(17) = 12069 p = 0010] in prominence tonal signalswere perceived as more annoying than the least (1st)prominent tonal signals

Figure 7 illustrates the mean annoyance ratings acrossbackground noise levels tonal frequencies and tonestrengths Summarizing these results the overall back-ground noise level does impact annoyance with higherlevels leading to greater annoyance The lower fre-quency tone generated greater annoyance ratings butone should note that the prominence levels of the125 Hz tone versus those of the 500 Hz tone used inthe study were not the same even though the relativedifferences from the threshold of tones presented inISO 1996-22007 are the same There was also a sig-nificant interaction effect between background noiselevel and tone frequency [F(17) = 3331 p = 0014

2p = 060] As plotted in Fig 7(d) the difference be-tween annoyance ratings of the 125 and 500 Hz toneswas greater with the 40 dBA background noise levelcondition than with the 55 dBA background noiselevel condition It appears that tonal frequency is lessrelated to annoyance at higher background noiselevels but plays a larger role at lower backgroundnoise levels

The data on tonal strength shows that higher tone levelsare linked to higher annoyance ratings analysis of the datato determine a threshold of annoyance is presented inFrancis et als study33 Noise sensitivity was expected tobe associated with annoyance but did not demonstrate sta-tistically significant effects in the ANOVA analysis as abetween-subjects factor This is attributed to the limitednumber of subjects in the study which was selected basedon a power analysis of previous annoyance results ratherthan noise sensitivity results

33 Correlations of Noise Metrics withSubjective Responses

The previous section showed that physical aspects ofthe noise signals (specifically loudness and tonality) werecorrelated with annoyance responses in this sectionassorted metrics for quantifying those physical aspectsare tested against the subjective responses Spearmansnonparametric correlation coefficients were calculatedbetween a number of noise metrics and the average par-ticipants perception ratings of loudness annoyance andTLX-avg The results have been analyzed in two ways

Fig 7mdashMean annoyance perception ratings plotted against (a) Background noise level (b) Tonalfrequency (c) Strength of the tones where Tone 1 indicates the least prominent tone andTone 5 indicates the most prominent tone and (d) Interaction of background noise leveland tonal frequency Error bars indicate one standard error

78 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

first with all twenty tonal signals included and then withthe average ratings for ten signals grouped separately bythe broadband background noise level (40 or 55 dBA)Table 4 presents correlation coefficients between all noisemetrics with the subjective perception responses

When analyzing all signals the noise metric thatdemonstrates the highest correlation coefficients withthe perceived loudness annoyance and TLX-avg rat-ings is ANSI loudness level Other loudness metricswere also significantly correlated to the perception rat-ings but the tonality metrics such as prominence ratiotone-to-noise ratio tonality audibility and Aures tonal-ity did not statistically correlate or had lower coefficientsthan loudness metrics This confirms that loudness is themost dominant factor in determining subjective percep-tion of noise

When the signals are grouped separately by broadbandbackground noise levels though tonality metrics didshow higher correlations with subjective ratings thanloudness metrics The coefficient values for the assortedtonality metrics are all very similar with no particularmetric clearly performing better than others Howeverwhen only looking for correlation coefficients with an-noyance tonal audibility showed slightly higher corre-lation coefficients than other tonality metrics (0888for 40 dBA BNL and 0891 for 55 dBA BNL) Aurestonality also showed high correlation with annoyancefrom 55 dBA BNL signals (0903) but it showed lowercorrelation than other metrics with 40 dBA BNL sig-nals (0709) The results indicate that when the broad-band background noise level is controlled or comparabletonality becomes a more influencing factor on annoyanceevaluation Figure 8 presents scatterplots of the averagedannoyance responses (a) with the ANSI loudness levelacross the entire group and (b) with tonal audibility sepa-rated by background noise level

For all cases combined metrics such as the JointNordic Method tone-corrected perceived noise leveland sound quality indicator did not show remarkablybetter performance than loudness metrics even thoughthese combined metrics were significantly related withannoyance ratings The results suggest that imposingpenalty values to loudness levels based on tonal strengthmay not be the most appropriate way to quantify overallsubjective annoyance of tonal noise Instead using sepa-rate metrics to account for tonality and loudness of build-ing mechanical noises is an effective way to relate to thesignals annoyance

34 Regression Model between Noise Metricsand Annoyance

Based on the results in Table 4 ANSI loudness leveland tonal audibility were selected to be used as predictors

Table 4mdashSpearmans correlation analysis of noisemetrics against subjective responses andSudoku puzzle task performance The resultsare analyzed first with all signals includedand then in two groups separated by back-ground noise level (40 or 55 dBA) Boldedvalues indicate metrics chosen for use inthe regression model based on their overallhigh significant correlation values

All signals (40 dBA and 55 dBA BNL)

Loudness Annoyance TLX-avgPR 0150 0186 0147TNR 0123 0081 0095ΔLta 0006 0056 0019Aures 0297 0359 0314dB 0805 0824 0772dBA 0866 0887 0842ANSI loudness 0946 0950 0926ISO loudness 0938 0952 0925PNL 0892 0920 0886PNLT 0869 0877 0826JNM 0840 0869 0818SQI 0904 0899 0856

40 dBA BNL only

PR 0794 0867 0782TNR 0794 0867 0782ΔLta 0778 0888 0815Aures 0673 0709 0697dB 0806 0939 0855dBA 0794 0927 0830ANSI loudness 0685 0745 0697ISO loudness 0685 0745 0697PNL 0685 0842 0867PNLT 0794 0830 0758JNM 0794 0927 0830SQI 0806 0806 0709

55 dBA BNL only

PR 0799 0867 0758TNR 0709 0845 0845ΔLta 0787 0891 0818Aures 0781 0903 0782dB 0715 0756 0530dBA 0707 0770 0564ANSI loudness 0878 0855 0709ISO loudness 0817 0867 0697PNL 0720 0806 0539PNLT 0744 0782 0527JNM 0707 0770 0564SQI 0689 0663 0444

Correlation is significant at the 001 level (2-tailed)Correlation is significant at the 005 level (2-tailed)

79Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

for a linear multiple regression model for annoyancebecause these two metrics resulted in among the stron-gest correlations with annoyance perception compared

to other noise metrics Equation (2) presents the multi-variate regression model with ANSI loudness level andtonal audibility

Annoyance frac14 1806 thorn 1164 ANSI Loudness soneeth THORNfrac12 thorn 0072 Tonal Audibility dBeth THORNfrac12

eth2THORN

Table 5 also presents standard error of coefficientsstandardized coefficients and statistical significance whenANSI loudness level was only used (in step 1) and whentonal audibility was also included (in step 2) in additionto the coefficient values for each predictor Standardizedb values indicate the number of standard deviations thatthe outcome annoyance will change as a result of onestandard deviation change in the predictor The R2 valuefor the first step model is 0943 which is a measure ofgoodness-of-fit of linear regression indicating that 943of the annoyance rating variance can be explained by theANSI loudness model only When including tonal audibil-ity as a second predictor the R2 value increased to 0962Even though this increase is small the multivariate re-gression model does significantly predict more variationin annoyance perception when including tonal audibilityas a second predictor for step 2 the ANSI loudnesslevel [t(17) = 20796 p lt 0001] and tonal audibility[t(17) = 2943 p = 0009] are both significant predictorsof annoyance Figure 9 illustrates a regression line withthe calculated linear model

The results of the correlation analysis and regressionmodel presented in this paper are in line with the find-ings from More and Daviesrsquo study8 which focused onaircraft flyover noise rather than building mechanicalsystem noise Their work focused only on annoyanceand used metric values that were exceeded some per-centage (often 5) of the time since their flyover

Fig 8mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals against (a) ANSIloudness level for all signals and(b) Tonal audibility for 40 and 55 dBABNL separately Dashed lines indicateregression lines of annoyance ratingprediction with regard to each metric

Table 5mdashLinear regression model of predictors for annoyance perception with 95 bias corrected and accel-erated confidence intervals reported in parentheses Confidence intervals and standard errors arebased on 1000 bootstrap samples Standardized b values indicate the number of standard deviationsthat the outcome annoyance will change as a result of one standard deviation change in the predictor

b Standarderror B

b p

Step 1Constant 3254(2305 4310) 0512 p = 0001ANSI loudness (sone) 1137(1004 1263) 0066 0971 p = 0001

Step 2Constant 1806(0498 3187) 0683 p = 0020ANSI loudness (sone) 1164(1043 1308) 0069 0994 p = 0001Tonal audibility (dB) 0072(0027 0111) 0021 0141 p = 0004

Note 0943 for Step 1 ΔR2 = 0019 for Step 2

80 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

signals varied in time They demonstrated that the bestregression model when using existing metrics to matchtheir annoyance responses included both a loudnessmetric and a tonality metric and they proposed revi-sion to the penalty values used for the Joint NordicMethod to improve its individual predictive capabilityThe authors feel though that it is not necessary tomodify a combined metric (like Joint Nordic Method)since using individual metrics for loudness and for to-nality in the proposed regression model herein demon-strated high correlations to the annoyance responseson their own

4 SUMMARYAND CONCLUSION

The purpose of this study was to investigate hownoise signals with varying degrees of prominent tonessimilar to those produced by building mechanicalequipment affect subjective annoyance perception andtask performance and to develop a prediction modelof annoyance using current noise metrics Subjectscompleted Sudoku puzzles and a questionnaire modi-fied from the NASA task load index to quantify theoverall workload caused by building mechanical noisein this study No statistically significant effect was foundbetween the tonal signals used in this study and task per-formance although there was a trend of decreasing accu-racy with increasing tone strengths based on correctSudoku puzzle answers The validity of the modified taskload index questionnaire was high based on its reliabilitycoefficient and testndashretest coefficient and the average re-sponse from the questionnaire was found to significantlycorrelate with perceived annoyance and loudness of thebackground noise signals A factorial repeated measure

ANOVA revealed that participants felt more annoyed withincreasing background noise level lower tone frequencyand higher tone strength Correlation analysis with noisemetrics and subjective perception ratings found that ANSIloudness level among all other loudness metrics correlatesmost strongly with annoyance perception while assortedtonality metrics showed relatively weaker but still statisti-cally significant correlations with annoyance A statisticallysignificant multivariate regression model with ANSI loud-ness level and tonal audibility has been developed whichdemonstrates an R2 value of 0962

While noise sensitivity of test subjects was surveyedno statistically significant relations between perceptionor performance results and noise sensitivity were foundlikely due to the limited number of test subjects Futurework in this area is suggested with more test subjectsand more tonal signals to understand better the roleof noise sensitivity Also tonal noises from actual buildingmechanical systems often demonstrate multiple tones whichmay be inharmonic or which can fluctuate in time addi-tional investigations using tonal signals that incorporatethese other factors are recommended

5 ACKNOWLEDGMENTS

Many thanks to the undergraduate research assis-tants Adam Steinbach and Kristin Hanna who assistedwith the study design subjective testing and data analysisPartial funding for this project came from an Institute ofNoise Control Engineering Undergraduate Research Grant

6 REFERENCES

1 International Organization for Standardization (ISO) ldquoAcousticsmdashDescription Measurement and Assessment of EnvironmentalNoisemdashPart 2 Determination of Environmental Noise LevelsrdquoInternational Standard ISO 1996ndash22007 International Organi-zation for Standardization (2007)

2 ldquoSeattle Municipal Code Chapter 2508 Noise Controlrdquo Seattle(2007)

3 ldquoNoise Control Ordinance of the County of Los Angelesrdquo LosAngeles County (1978)

4 ldquoOrdinance of Minnesota Chapter 389 ndash Noiserdquo Minnesota(2008)

5 NY ADC LAW 27-770 NY code ndash Section 27-770 ldquoNoiseControl of Mechanical Equipmentrdquo New York (2006)

6 RP Hellman ldquoGrowth rate of loudness annoyance and noisi-ness as a function of tone location within the noise spectrumrdquoJ Acoust Soc Am 75(1) 209ndash218 (1984)

7 RP Hellman ldquoPerceived magnitude of two-tone-noise com-plexes loudness annoyance and noisinessrdquo J Acoust SocAm 77(4) 1497ndash1504 (1985)

8 S More and P Davies ldquoHuman responses to the tonalness of air-craft noiserdquo Noise Control Engr J 58(4) 420ndash440 (2010)

9 KH Lee P Davies and AM Surprenant ldquoQuantification ofthe tonal prominence of complex tones in machinery noiserdquoNoiseCon04 (2004)

Fig 9mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals plotted against theproposed linear regression model ofannoyance perception (dashed) basedon ANSI loudness level and tonalaudibility (R2 = 096)

81Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

10 A Hastings H Lee P Davies and AM Surprenant ldquoMeasure-ment of the attributes of complex tonal components commonlyfound in product soundrdquo Noise Control Engr J 51(4) 195ndash209(2003)

11 EE Ryherd and LM Wang ldquoImplications of human perfor-mance and perception under tonal noise conditions on indoornoise criteriardquo J Acoust Soc Am 124(1) 218ndash226 (2008)

12 P Susini S McAdams S Winsberg I Perry S Vieillard andX Rodet ldquoCharacterizing the sound quality of air-conditioningnoiserdquo Appl Acoust 65(8) 763ndash790 (2004)

13 B Berglund P Hassmeacuten and A Preis ldquoAnnoyance and spectralcontrast are cues for similarity and preference of soundsrdquo J SoundVibr 250(1) 53ndash64 (2002)

14 U Landstroumlm E Aringkerlund A Kjellberg and M Tesarz ldquoEx-posure levels tonal components and noise annoyance in work-ing environmentsrdquo Environ Int 21(3) 265ndash275 (1995)

15 U Landstroumlm A Kjellberg and M Bystroumlm ldquoAcceptablelevels of sounds with different spectral characteristics duringthe performance of a simple and a complex non-auditory taskrdquoJ Sound Vibr 160(3) 533ndash542 (1993)

16 D Laird ldquoThe influence of noise on production and fatigue asrelated to pitch sensation level and steadiness of the noiserdquo JAppl Psychol 17(3) 320ndash330 (1933)

17 JV Grjmaldi ldquoSensori-motor performance under varying noiseconditionsrdquo Ergonomics 2(1) 34ndash43 (1958)

18 K Holmberg U Landstrom and A Kjellberg ldquoEffects of ven-tilation noise due to frequency characteristic and sound levelrdquoJ Low Freq Noise Vib 12(4) 115ndash122 (1993)

19 International Organization for Standardization (ISO) ldquoAcousticsmdashAssessment of noise annoyance by means of social and socio-acoustic surveysrdquo International Standard ISOTS 15666 2003International Organization for Standardization (2003)

20 TH Pedersen ldquoThe ldquoGenlydrdquo noise annoyance model dose-response relationships modelled by logistic functionsrdquo DELTAHoslashrsholm Danmark (2007)

21 CMarquis-Favre E Premat andDAubreacutee ldquoNoise and its effectsmdasha review on qualitative aspects of sound Part II noise and annoy-ancerdquo Acta Acust united with Acust 91(4) 626ndash642 (2005)

22 American National Standards Institute (ANSI) ldquoAcousticsmdashMeasurement of Airborne Noise Emitted by Information Technol-ogy and Telecommunications EquipmentmdashPart 1 Determinationof Sound Power Level and Emission Sound Pressure LevelrdquoAmerican National Standards Institute ANSIASA S1210-2010Part 1 American Society of America (2010)

23 W Aures ldquoThe sensory euphony as a function of auditory sen-sationsrdquo Acustica 58(5) 282ndash290 (1985)

24 American National Standards Institute (ANSI) ldquoProcedure forthe Computation of Loudness of Steady Soundsrdquo AmericanNational Standards Institute ANSI S34-2007 Acoustical Soci-ety of America (2007)

25 ldquoAcousticsmdashMethod for Calculating Loudness Levelrdquo Interna-tional Standard ISO 5321975 International Organization forStandardization (1975)

26 E Zwicker ldquoSubdivision of the audible frequency range intocritical bands (Frequenzgruppen)rdquo J Acoust Soc Am 33(2)248ndash248 (1961)

27 BR Glasberg and BCJ Moore ldquoPrediction of absolutethresholds and equal loudness contours using a modified loud-ness modelrdquo J Acoust Soc Am 120 585ndash588 (2006)

28 Federal Aviation Administration ldquoPart 36 Noise StandardsAircraft Type and Airworthiness Certificationrdquo Federal Avia-tion Regulations Federal Aviation Administration (1969)

29 Air-Conditioning Heating and Refrigeration Institute (AHRI)ldquoSound Quality Evaluation Procedures for Air-Conditioningand Refrigeration Equipmentrdquo Air-Conditioning Heating andRefrigeration Institute AHRIANSI 1140-2012 Air-ConditioningHeating and Refrigeration Institute (2012)

30 WE Blazier ldquoRevised noise criteria for application in theacoustical design and rating of HVAC systemsrdquo Noise ControlEngr 16(2) 64ndash73 (1981)

31 F Faul E Erdfelder A Buchner and A-G Lang ldquoStatisticalpower analyses using GPower 31 tests for correlation and re-gression analysesrdquo Behav Res Methods 41 1149ndash1160 (2009)

32 M Schutte A Marks E Wenning and B Griefahn ldquoThe de-velopment of the noise sensitivity questionnairerdquo Noise Heal9(34) 15 (2007)

33 J Francis J Lee A Steinbach and LM Wang ldquoDeterminingannoyance thresholds of tones in noiserdquo ASHRAE Trans120 (2014)

34 JW Grabbe ldquoSudoku and working memory performance forolder adultsrdquo Act Adapt Aging 35(3) 241ndash254 (2011)

35 HS Chang and JM Gibson ldquoThe oddndasheven effect in Sudokupuzzles effects of working memory aging and experiencerdquoAm J Psychol 124(3) 313ndash324 (2011)

36 ldquoNASA TLX Task Load Indexrdquo [Online] Available at httphumansystemsarcnasagovgroupstlx (accessed September 92015)

37 RFS Job J Hatfield NL Carter P Peploe R Taylor andS Morrell ldquoGeneral scales of community reaction to noise(dissatisfaction and perceived affectedness) are more reliablethan scales of annoyancerdquo J Acoust Soc Am 110(2) 939ndash946(2001)

82 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

sample size was then found to be six participants to achieve80 power (1 b) at two-sided 5 significance level (a)Based on this finding and available research funds a test-ing plan was designed to assess 22 signals across tentest subjects

All participants completed an orientation session includ-ing a hearing screening test before participation and dem-onstrated normal hearing with thresholds below 25 dBhearing level (HL) from 125 Hz to 8 kHz The noisesensitivity of each participant was also measured by areduced version (13 items only) of the Noise-Sensitivity-Questionnaire (NoiSeQ) by Schutte et al32 during the ori-entation session The participants were asked to answereach item using a four-level rating scale (strongly agree = 1slightly agree = 2 slightly disagree = 3 strongly dis-agree = 4) The responses were averaged across all itemsto form a composite scale to quantify the noise sensitivityfor each participant

The main test consisted of two parts a direct assess-ment with task (part A) and a magnitude adjustment test(part B) The results of part B have been presented inanother paper33 and hence are not included herein In partA participants were asked to complete as many Sudokunumber puzzles as possible while exposed to a broad-band noise signal some with assorted tonal compo-nents for 10 minutes Sudoku puzzles were selected asthe measure of task performance as they are compact toadminister easy to explain to test participants and havebeen used as a measure of task performance in otherstudies with results showing significant relationship withworking memory3435 All participants practiced solvingSudoku puzzles during the orientation session before par-ticipating in the main test and the difficulty of all Sudokupuzzles in the main test was held constant The puzzleswere all nine by nine with forty of the eighty-one grids be-ing prefilled with numbers

After spending 10 minutes solving the Sudoku puz-zles the subjects answered five questions on a subjec-tive questionnaire about the noise they had just heardThe questionnaire was a modified version of the NASAtask load index36 The original NASA task load index isdivided into six subscales mental demand physical de-mand temporal demand performance effort and frus-tration In this study the questions on physical demandtemporal demand and frustration were not included in-stead questions were added on rating loudness and annoy-ance incurred by noise as shown in Table 2 Participantsresponded to each question based on a 21-point scale (tomatch the scale from the original NASA task load index)on a paper form

Part A consisted of ten 30-minute sessions that werecompleted by each subject individually on differentdays Within each 30-minute session subjects were ex-posed to three noise signals (each for 10 minutes) and

thus completed three sequences of Sudoku puzzles (dif-ferent puzzles each time) followed by the questionnaireTo minimize the influence of back-to-back comparisonsof tonal noise conditions a neutral background noisecondition without any tonal components was used asthe second signal within each 30-minute test sessionWithin a single 30-minute test session the noise levelof the broadband noise without consideration for anytonal components remained at a constant level either40 or 55 dBA The presentation order of the backgroundnoise levels and tonal test signals was carefully balancedacross all subjects using a Latin square design

Two task performance measures were gathered by(1) counting the amount of Sudoku puzzles a subjectcompleted within a 10-minute trial with partial comple-tions included as well and (2) quantifying the accuracyof the puzzle answers in terms of correct numbers amongthose answered in a puzzle The maximum and mini-mum number of Sudoku puzzles participants completedin one 10-minute session were 28 and 03 and the max-imum and minimum accuracy of the puzzles were 100and 69

3 RESULTS AND DISCUSSION

The reliability of each participants responses wasdetermined from correlation analysis of the participantsindividual annoyance responses to a loudness metrictonality metric and average ratings across participants8Figure 4 presents correlation coefficients of each parti-cipants annoyance responses to the ANSI loudnesslevel tonal audibility and mean values across partici-pants Two participants responses (number 6 and 8)were excluded from all analyses because they ratedresponses randomly regardless of sound characteristics(correlation value lt02) The subject-to-loudness co-efficient of participant 6 was 017 and the subject-to-

Table 2mdashItems from the subjective questionnaireas modified from the NASA task loadindex

Description Questions

Mental demand 1 How mentally demandingwas the task

Overall performance 2 How successful wereyou in accomplishing whatyou were asked to do

Effort 3 How hard did you haveto work to accomplish yourlevel of performance

Loudness 4 How loud was the noiseAnnoyance 5 How annoying was the noise

75Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

tonality correlation coefficient of participant 8 was 003All subsequent analyses are based on the remaining eightsubjects which is still above the minimum sample size ofsix recommended from the a priori power analysis Addi-tionally outlier responses of annoyance and task perfor-mance scores were excluded from the statistical analysispresented below The outliers were identified using the cri-terion of being beyond three standard deviations from theaverage across participants Two outliers of annoyance rat-ings and three outliers of task performance scores wereexcluded for analyses based on this criterion

31 Relating Task Performance to SubjectiveResponses and to Noise Attributes

The task performance measures related to the Sudokupuzzles were correlated to the participants subjective

responses on the modified NASA task load index ques-tionnaires (Table 3) Spearmans correlation (r) was utilizedbecause not all of the variables met the assumption ofhaving a normal distribution with the sample size uti-lized An additional ldquoTLX-avgrdquo score was calculated asthe averaged value of all five items from the modified sur-vey to represent an overall rating of subjective task loadperception induced by noise exposure Since the task diffi-culty was held constant with equivalently difficult Sudokupuzzles throughout the experiment the variations in sub-jective ratings observed within subjects can be consideredas the result of varying background noise conditions Jobet al37 have recommended against using a single questionitem about annoyance because of its reduced validity con-sequently the composite modified Noise TLX rating isproposed as an alternative in this laboratory study Witha Cronbachs a coefficient for the reliability of 082 anda testndashretest correlation of the Noise TLX measure forthe stability of 077 the ldquoTLX-avgrdquo questionnaire wasfound to be internally consistent and stable over timeand thus suitable for the purpose of this test

As Table 3 indicates most of the subjective responseswere significantly correlated with each other Specificallyof interest the mental demand responses showed highcorrelations with perceptions of loudness and annoy-ance of the noise and as expected loudness and annoy-ance ratings were significantly correlated with each other(r = 0948) The only statistically significant correla-tion between a task performance result and a subjec-tive response was between ldquoaccuracyrdquo (accuracy ratesof participants puzzle answers) and responses to theldquoperformancerdquo question on the questionnaire (r =0483)

Figures 5 and 6 present the averaged task perfor-mance of the accuracy and number of completed puzzles

Fig 4mdashCorrelation coefficients of eachparticipants annoyance responses toeach signals ANSI loudness tonalaudibility and group average

Table 3mdashSpearmans correlation analysis of the subjective responses and Sudoku puzzle task performancemeasures TLX-avg is the average value of the responses to all five questions on the modified taskload index questionnaire ldquoNo of completedrdquo refers to the number of completed puzzles for eachtrial and ldquoaccuracyrdquo indicates accuracy rates of participants puzzle answers

Mentaldemand

Performance Effort Loudness Annoyance TLX-avg

No ofcompleted

Accuracy

Mental demand ndashPerformance 0260 ndashEffort 0610 0496 ndashLoudness 0501 0105 0230 ndashAnnoyance 0528 0162 0398 0948 ndashTLXndashavg 0631 0374 0601 0880 0956 ndashNo of puzzles completed 0317 0438 0394 0074 0020 0171 ndashAccuracy 0105 0483 0071 0289 0252 0330 0080 ndash

Correlation is significant at the 001 level (2-tailed)Correlation is significant at the 005 level (2-tailed)

76 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

against the physical attributes of the noise signals in-cluding background noise level tone frequency andthe five levels of tone strengths The repeated measureANOVA (analysis of variance) confirms that there wereno statistically significant differences between task per-formances across the various noise attributes Thussubjects did not complete more puzzles or have higheraccuracy under any particular tonal frequency back-ground noise level or tone strength although there

appears to be a slight tendency of lower accuracy withgreater tone strength

32 Relating Noise Attributes toAnnoyance Responses

To understand how the physical aspects of the noisesignals (background noise level tone frequency andtonal strength) related to annoyance a three-way repeated

Fig 5mdashAveraged accuracy of Sudoku puzzle answers as task performance scores plotted against(a) Background noise level (b) Tonal frequency and (c) Strength of the tones where Tone1 indicates the least prominent tone and Tone 5 indicates the most prominent tone Errorbars indicate one standard error

Fig 6mdashAveraged number of completed Sudoku puzzles as task performance scores plotted against(a) Background noise level (b) Tonal frequency and (c) Strength of the tones where Tone1 indicates the least prominent tone and Tone 5 indicates the most prominent tone Errorbars indicate one standard error

77Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

measure ANOVAwas conducted Mauchlys test indicatedthat the assumption of sphericity had been met for themain effects of tonal strength and its interactions withtone frequency and background noise level The analysisshows a significant main effect of background noise level[F(17) = 8261 p lt 0001 2p = 092] tone frequency

[F(17) = 2001 p = 0003 2p = 074] and tonal strength

[F(428) = 476 p = 0005 2p = 041] on annoyanceThe main analysis shows that the 55 dBA based

tonal signals were significantly more annoying than40 dBA based tonal signals and that the 125 Hz tonalsignals were significantly more annoying than 500 Hztonal signals Contrast comparisons reveal that the 4thhighest [F(17) = 10420 p = 0014] and 5th highest[F(17) = 12069 p = 0010] in prominence tonal signalswere perceived as more annoying than the least (1st)prominent tonal signals

Figure 7 illustrates the mean annoyance ratings acrossbackground noise levels tonal frequencies and tonestrengths Summarizing these results the overall back-ground noise level does impact annoyance with higherlevels leading to greater annoyance The lower fre-quency tone generated greater annoyance ratings butone should note that the prominence levels of the125 Hz tone versus those of the 500 Hz tone used inthe study were not the same even though the relativedifferences from the threshold of tones presented inISO 1996-22007 are the same There was also a sig-nificant interaction effect between background noiselevel and tone frequency [F(17) = 3331 p = 0014

2p = 060] As plotted in Fig 7(d) the difference be-tween annoyance ratings of the 125 and 500 Hz toneswas greater with the 40 dBA background noise levelcondition than with the 55 dBA background noiselevel condition It appears that tonal frequency is lessrelated to annoyance at higher background noiselevels but plays a larger role at lower backgroundnoise levels

The data on tonal strength shows that higher tone levelsare linked to higher annoyance ratings analysis of the datato determine a threshold of annoyance is presented inFrancis et als study33 Noise sensitivity was expected tobe associated with annoyance but did not demonstrate sta-tistically significant effects in the ANOVA analysis as abetween-subjects factor This is attributed to the limitednumber of subjects in the study which was selected basedon a power analysis of previous annoyance results ratherthan noise sensitivity results

33 Correlations of Noise Metrics withSubjective Responses

The previous section showed that physical aspects ofthe noise signals (specifically loudness and tonality) werecorrelated with annoyance responses in this sectionassorted metrics for quantifying those physical aspectsare tested against the subjective responses Spearmansnonparametric correlation coefficients were calculatedbetween a number of noise metrics and the average par-ticipants perception ratings of loudness annoyance andTLX-avg The results have been analyzed in two ways

Fig 7mdashMean annoyance perception ratings plotted against (a) Background noise level (b) Tonalfrequency (c) Strength of the tones where Tone 1 indicates the least prominent tone andTone 5 indicates the most prominent tone and (d) Interaction of background noise leveland tonal frequency Error bars indicate one standard error

78 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

first with all twenty tonal signals included and then withthe average ratings for ten signals grouped separately bythe broadband background noise level (40 or 55 dBA)Table 4 presents correlation coefficients between all noisemetrics with the subjective perception responses

When analyzing all signals the noise metric thatdemonstrates the highest correlation coefficients withthe perceived loudness annoyance and TLX-avg rat-ings is ANSI loudness level Other loudness metricswere also significantly correlated to the perception rat-ings but the tonality metrics such as prominence ratiotone-to-noise ratio tonality audibility and Aures tonal-ity did not statistically correlate or had lower coefficientsthan loudness metrics This confirms that loudness is themost dominant factor in determining subjective percep-tion of noise

When the signals are grouped separately by broadbandbackground noise levels though tonality metrics didshow higher correlations with subjective ratings thanloudness metrics The coefficient values for the assortedtonality metrics are all very similar with no particularmetric clearly performing better than others Howeverwhen only looking for correlation coefficients with an-noyance tonal audibility showed slightly higher corre-lation coefficients than other tonality metrics (0888for 40 dBA BNL and 0891 for 55 dBA BNL) Aurestonality also showed high correlation with annoyancefrom 55 dBA BNL signals (0903) but it showed lowercorrelation than other metrics with 40 dBA BNL sig-nals (0709) The results indicate that when the broad-band background noise level is controlled or comparabletonality becomes a more influencing factor on annoyanceevaluation Figure 8 presents scatterplots of the averagedannoyance responses (a) with the ANSI loudness levelacross the entire group and (b) with tonal audibility sepa-rated by background noise level

For all cases combined metrics such as the JointNordic Method tone-corrected perceived noise leveland sound quality indicator did not show remarkablybetter performance than loudness metrics even thoughthese combined metrics were significantly related withannoyance ratings The results suggest that imposingpenalty values to loudness levels based on tonal strengthmay not be the most appropriate way to quantify overallsubjective annoyance of tonal noise Instead using sepa-rate metrics to account for tonality and loudness of build-ing mechanical noises is an effective way to relate to thesignals annoyance

34 Regression Model between Noise Metricsand Annoyance

Based on the results in Table 4 ANSI loudness leveland tonal audibility were selected to be used as predictors

Table 4mdashSpearmans correlation analysis of noisemetrics against subjective responses andSudoku puzzle task performance The resultsare analyzed first with all signals includedand then in two groups separated by back-ground noise level (40 or 55 dBA) Boldedvalues indicate metrics chosen for use inthe regression model based on their overallhigh significant correlation values

All signals (40 dBA and 55 dBA BNL)

Loudness Annoyance TLX-avgPR 0150 0186 0147TNR 0123 0081 0095ΔLta 0006 0056 0019Aures 0297 0359 0314dB 0805 0824 0772dBA 0866 0887 0842ANSI loudness 0946 0950 0926ISO loudness 0938 0952 0925PNL 0892 0920 0886PNLT 0869 0877 0826JNM 0840 0869 0818SQI 0904 0899 0856

40 dBA BNL only

PR 0794 0867 0782TNR 0794 0867 0782ΔLta 0778 0888 0815Aures 0673 0709 0697dB 0806 0939 0855dBA 0794 0927 0830ANSI loudness 0685 0745 0697ISO loudness 0685 0745 0697PNL 0685 0842 0867PNLT 0794 0830 0758JNM 0794 0927 0830SQI 0806 0806 0709

55 dBA BNL only

PR 0799 0867 0758TNR 0709 0845 0845ΔLta 0787 0891 0818Aures 0781 0903 0782dB 0715 0756 0530dBA 0707 0770 0564ANSI loudness 0878 0855 0709ISO loudness 0817 0867 0697PNL 0720 0806 0539PNLT 0744 0782 0527JNM 0707 0770 0564SQI 0689 0663 0444

Correlation is significant at the 001 level (2-tailed)Correlation is significant at the 005 level (2-tailed)

79Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

for a linear multiple regression model for annoyancebecause these two metrics resulted in among the stron-gest correlations with annoyance perception compared

to other noise metrics Equation (2) presents the multi-variate regression model with ANSI loudness level andtonal audibility

Annoyance frac14 1806 thorn 1164 ANSI Loudness soneeth THORNfrac12 thorn 0072 Tonal Audibility dBeth THORNfrac12

eth2THORN

Table 5 also presents standard error of coefficientsstandardized coefficients and statistical significance whenANSI loudness level was only used (in step 1) and whentonal audibility was also included (in step 2) in additionto the coefficient values for each predictor Standardizedb values indicate the number of standard deviations thatthe outcome annoyance will change as a result of onestandard deviation change in the predictor The R2 valuefor the first step model is 0943 which is a measure ofgoodness-of-fit of linear regression indicating that 943of the annoyance rating variance can be explained by theANSI loudness model only When including tonal audibil-ity as a second predictor the R2 value increased to 0962Even though this increase is small the multivariate re-gression model does significantly predict more variationin annoyance perception when including tonal audibilityas a second predictor for step 2 the ANSI loudnesslevel [t(17) = 20796 p lt 0001] and tonal audibility[t(17) = 2943 p = 0009] are both significant predictorsof annoyance Figure 9 illustrates a regression line withthe calculated linear model

The results of the correlation analysis and regressionmodel presented in this paper are in line with the find-ings from More and Daviesrsquo study8 which focused onaircraft flyover noise rather than building mechanicalsystem noise Their work focused only on annoyanceand used metric values that were exceeded some per-centage (often 5) of the time since their flyover

Fig 8mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals against (a) ANSIloudness level for all signals and(b) Tonal audibility for 40 and 55 dBABNL separately Dashed lines indicateregression lines of annoyance ratingprediction with regard to each metric

Table 5mdashLinear regression model of predictors for annoyance perception with 95 bias corrected and accel-erated confidence intervals reported in parentheses Confidence intervals and standard errors arebased on 1000 bootstrap samples Standardized b values indicate the number of standard deviationsthat the outcome annoyance will change as a result of one standard deviation change in the predictor

b Standarderror B

b p

Step 1Constant 3254(2305 4310) 0512 p = 0001ANSI loudness (sone) 1137(1004 1263) 0066 0971 p = 0001

Step 2Constant 1806(0498 3187) 0683 p = 0020ANSI loudness (sone) 1164(1043 1308) 0069 0994 p = 0001Tonal audibility (dB) 0072(0027 0111) 0021 0141 p = 0004

Note 0943 for Step 1 ΔR2 = 0019 for Step 2

80 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

signals varied in time They demonstrated that the bestregression model when using existing metrics to matchtheir annoyance responses included both a loudnessmetric and a tonality metric and they proposed revi-sion to the penalty values used for the Joint NordicMethod to improve its individual predictive capabilityThe authors feel though that it is not necessary tomodify a combined metric (like Joint Nordic Method)since using individual metrics for loudness and for to-nality in the proposed regression model herein demon-strated high correlations to the annoyance responseson their own

4 SUMMARYAND CONCLUSION

The purpose of this study was to investigate hownoise signals with varying degrees of prominent tonessimilar to those produced by building mechanicalequipment affect subjective annoyance perception andtask performance and to develop a prediction modelof annoyance using current noise metrics Subjectscompleted Sudoku puzzles and a questionnaire modi-fied from the NASA task load index to quantify theoverall workload caused by building mechanical noisein this study No statistically significant effect was foundbetween the tonal signals used in this study and task per-formance although there was a trend of decreasing accu-racy with increasing tone strengths based on correctSudoku puzzle answers The validity of the modified taskload index questionnaire was high based on its reliabilitycoefficient and testndashretest coefficient and the average re-sponse from the questionnaire was found to significantlycorrelate with perceived annoyance and loudness of thebackground noise signals A factorial repeated measure

ANOVA revealed that participants felt more annoyed withincreasing background noise level lower tone frequencyand higher tone strength Correlation analysis with noisemetrics and subjective perception ratings found that ANSIloudness level among all other loudness metrics correlatesmost strongly with annoyance perception while assortedtonality metrics showed relatively weaker but still statisti-cally significant correlations with annoyance A statisticallysignificant multivariate regression model with ANSI loud-ness level and tonal audibility has been developed whichdemonstrates an R2 value of 0962

While noise sensitivity of test subjects was surveyedno statistically significant relations between perceptionor performance results and noise sensitivity were foundlikely due to the limited number of test subjects Futurework in this area is suggested with more test subjectsand more tonal signals to understand better the roleof noise sensitivity Also tonal noises from actual buildingmechanical systems often demonstrate multiple tones whichmay be inharmonic or which can fluctuate in time addi-tional investigations using tonal signals that incorporatethese other factors are recommended

5 ACKNOWLEDGMENTS

Many thanks to the undergraduate research assis-tants Adam Steinbach and Kristin Hanna who assistedwith the study design subjective testing and data analysisPartial funding for this project came from an Institute ofNoise Control Engineering Undergraduate Research Grant

6 REFERENCES

1 International Organization for Standardization (ISO) ldquoAcousticsmdashDescription Measurement and Assessment of EnvironmentalNoisemdashPart 2 Determination of Environmental Noise LevelsrdquoInternational Standard ISO 1996ndash22007 International Organi-zation for Standardization (2007)

2 ldquoSeattle Municipal Code Chapter 2508 Noise Controlrdquo Seattle(2007)

3 ldquoNoise Control Ordinance of the County of Los Angelesrdquo LosAngeles County (1978)

4 ldquoOrdinance of Minnesota Chapter 389 ndash Noiserdquo Minnesota(2008)

5 NY ADC LAW 27-770 NY code ndash Section 27-770 ldquoNoiseControl of Mechanical Equipmentrdquo New York (2006)

6 RP Hellman ldquoGrowth rate of loudness annoyance and noisi-ness as a function of tone location within the noise spectrumrdquoJ Acoust Soc Am 75(1) 209ndash218 (1984)

7 RP Hellman ldquoPerceived magnitude of two-tone-noise com-plexes loudness annoyance and noisinessrdquo J Acoust SocAm 77(4) 1497ndash1504 (1985)

8 S More and P Davies ldquoHuman responses to the tonalness of air-craft noiserdquo Noise Control Engr J 58(4) 420ndash440 (2010)

9 KH Lee P Davies and AM Surprenant ldquoQuantification ofthe tonal prominence of complex tones in machinery noiserdquoNoiseCon04 (2004)

Fig 9mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals plotted against theproposed linear regression model ofannoyance perception (dashed) basedon ANSI loudness level and tonalaudibility (R2 = 096)

81Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

10 A Hastings H Lee P Davies and AM Surprenant ldquoMeasure-ment of the attributes of complex tonal components commonlyfound in product soundrdquo Noise Control Engr J 51(4) 195ndash209(2003)

11 EE Ryherd and LM Wang ldquoImplications of human perfor-mance and perception under tonal noise conditions on indoornoise criteriardquo J Acoust Soc Am 124(1) 218ndash226 (2008)

12 P Susini S McAdams S Winsberg I Perry S Vieillard andX Rodet ldquoCharacterizing the sound quality of air-conditioningnoiserdquo Appl Acoust 65(8) 763ndash790 (2004)

13 B Berglund P Hassmeacuten and A Preis ldquoAnnoyance and spectralcontrast are cues for similarity and preference of soundsrdquo J SoundVibr 250(1) 53ndash64 (2002)

14 U Landstroumlm E Aringkerlund A Kjellberg and M Tesarz ldquoEx-posure levels tonal components and noise annoyance in work-ing environmentsrdquo Environ Int 21(3) 265ndash275 (1995)

15 U Landstroumlm A Kjellberg and M Bystroumlm ldquoAcceptablelevels of sounds with different spectral characteristics duringthe performance of a simple and a complex non-auditory taskrdquoJ Sound Vibr 160(3) 533ndash542 (1993)

16 D Laird ldquoThe influence of noise on production and fatigue asrelated to pitch sensation level and steadiness of the noiserdquo JAppl Psychol 17(3) 320ndash330 (1933)

17 JV Grjmaldi ldquoSensori-motor performance under varying noiseconditionsrdquo Ergonomics 2(1) 34ndash43 (1958)

18 K Holmberg U Landstrom and A Kjellberg ldquoEffects of ven-tilation noise due to frequency characteristic and sound levelrdquoJ Low Freq Noise Vib 12(4) 115ndash122 (1993)

19 International Organization for Standardization (ISO) ldquoAcousticsmdashAssessment of noise annoyance by means of social and socio-acoustic surveysrdquo International Standard ISOTS 15666 2003International Organization for Standardization (2003)

20 TH Pedersen ldquoThe ldquoGenlydrdquo noise annoyance model dose-response relationships modelled by logistic functionsrdquo DELTAHoslashrsholm Danmark (2007)

21 CMarquis-Favre E Premat andDAubreacutee ldquoNoise and its effectsmdasha review on qualitative aspects of sound Part II noise and annoy-ancerdquo Acta Acust united with Acust 91(4) 626ndash642 (2005)

22 American National Standards Institute (ANSI) ldquoAcousticsmdashMeasurement of Airborne Noise Emitted by Information Technol-ogy and Telecommunications EquipmentmdashPart 1 Determinationof Sound Power Level and Emission Sound Pressure LevelrdquoAmerican National Standards Institute ANSIASA S1210-2010Part 1 American Society of America (2010)

23 W Aures ldquoThe sensory euphony as a function of auditory sen-sationsrdquo Acustica 58(5) 282ndash290 (1985)

24 American National Standards Institute (ANSI) ldquoProcedure forthe Computation of Loudness of Steady Soundsrdquo AmericanNational Standards Institute ANSI S34-2007 Acoustical Soci-ety of America (2007)

25 ldquoAcousticsmdashMethod for Calculating Loudness Levelrdquo Interna-tional Standard ISO 5321975 International Organization forStandardization (1975)

26 E Zwicker ldquoSubdivision of the audible frequency range intocritical bands (Frequenzgruppen)rdquo J Acoust Soc Am 33(2)248ndash248 (1961)

27 BR Glasberg and BCJ Moore ldquoPrediction of absolutethresholds and equal loudness contours using a modified loud-ness modelrdquo J Acoust Soc Am 120 585ndash588 (2006)

28 Federal Aviation Administration ldquoPart 36 Noise StandardsAircraft Type and Airworthiness Certificationrdquo Federal Avia-tion Regulations Federal Aviation Administration (1969)

29 Air-Conditioning Heating and Refrigeration Institute (AHRI)ldquoSound Quality Evaluation Procedures for Air-Conditioningand Refrigeration Equipmentrdquo Air-Conditioning Heating andRefrigeration Institute AHRIANSI 1140-2012 Air-ConditioningHeating and Refrigeration Institute (2012)

30 WE Blazier ldquoRevised noise criteria for application in theacoustical design and rating of HVAC systemsrdquo Noise ControlEngr 16(2) 64ndash73 (1981)

31 F Faul E Erdfelder A Buchner and A-G Lang ldquoStatisticalpower analyses using GPower 31 tests for correlation and re-gression analysesrdquo Behav Res Methods 41 1149ndash1160 (2009)

32 M Schutte A Marks E Wenning and B Griefahn ldquoThe de-velopment of the noise sensitivity questionnairerdquo Noise Heal9(34) 15 (2007)

33 J Francis J Lee A Steinbach and LM Wang ldquoDeterminingannoyance thresholds of tones in noiserdquo ASHRAE Trans120 (2014)

34 JW Grabbe ldquoSudoku and working memory performance forolder adultsrdquo Act Adapt Aging 35(3) 241ndash254 (2011)

35 HS Chang and JM Gibson ldquoThe oddndasheven effect in Sudokupuzzles effects of working memory aging and experiencerdquoAm J Psychol 124(3) 313ndash324 (2011)

36 ldquoNASA TLX Task Load Indexrdquo [Online] Available at httphumansystemsarcnasagovgroupstlx (accessed September 92015)

37 RFS Job J Hatfield NL Carter P Peploe R Taylor andS Morrell ldquoGeneral scales of community reaction to noise(dissatisfaction and perceived affectedness) are more reliablethan scales of annoyancerdquo J Acoust Soc Am 110(2) 939ndash946(2001)

82 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

tonality correlation coefficient of participant 8 was 003All subsequent analyses are based on the remaining eightsubjects which is still above the minimum sample size ofsix recommended from the a priori power analysis Addi-tionally outlier responses of annoyance and task perfor-mance scores were excluded from the statistical analysispresented below The outliers were identified using the cri-terion of being beyond three standard deviations from theaverage across participants Two outliers of annoyance rat-ings and three outliers of task performance scores wereexcluded for analyses based on this criterion

31 Relating Task Performance to SubjectiveResponses and to Noise Attributes

The task performance measures related to the Sudokupuzzles were correlated to the participants subjective

responses on the modified NASA task load index ques-tionnaires (Table 3) Spearmans correlation (r) was utilizedbecause not all of the variables met the assumption ofhaving a normal distribution with the sample size uti-lized An additional ldquoTLX-avgrdquo score was calculated asthe averaged value of all five items from the modified sur-vey to represent an overall rating of subjective task loadperception induced by noise exposure Since the task diffi-culty was held constant with equivalently difficult Sudokupuzzles throughout the experiment the variations in sub-jective ratings observed within subjects can be consideredas the result of varying background noise conditions Jobet al37 have recommended against using a single questionitem about annoyance because of its reduced validity con-sequently the composite modified Noise TLX rating isproposed as an alternative in this laboratory study Witha Cronbachs a coefficient for the reliability of 082 anda testndashretest correlation of the Noise TLX measure forthe stability of 077 the ldquoTLX-avgrdquo questionnaire wasfound to be internally consistent and stable over timeand thus suitable for the purpose of this test

As Table 3 indicates most of the subjective responseswere significantly correlated with each other Specificallyof interest the mental demand responses showed highcorrelations with perceptions of loudness and annoy-ance of the noise and as expected loudness and annoy-ance ratings were significantly correlated with each other(r = 0948) The only statistically significant correla-tion between a task performance result and a subjec-tive response was between ldquoaccuracyrdquo (accuracy ratesof participants puzzle answers) and responses to theldquoperformancerdquo question on the questionnaire (r =0483)

Figures 5 and 6 present the averaged task perfor-mance of the accuracy and number of completed puzzles

Fig 4mdashCorrelation coefficients of eachparticipants annoyance responses toeach signals ANSI loudness tonalaudibility and group average

Table 3mdashSpearmans correlation analysis of the subjective responses and Sudoku puzzle task performancemeasures TLX-avg is the average value of the responses to all five questions on the modified taskload index questionnaire ldquoNo of completedrdquo refers to the number of completed puzzles for eachtrial and ldquoaccuracyrdquo indicates accuracy rates of participants puzzle answers

Mentaldemand

Performance Effort Loudness Annoyance TLX-avg

No ofcompleted

Accuracy

Mental demand ndashPerformance 0260 ndashEffort 0610 0496 ndashLoudness 0501 0105 0230 ndashAnnoyance 0528 0162 0398 0948 ndashTLXndashavg 0631 0374 0601 0880 0956 ndashNo of puzzles completed 0317 0438 0394 0074 0020 0171 ndashAccuracy 0105 0483 0071 0289 0252 0330 0080 ndash

Correlation is significant at the 001 level (2-tailed)Correlation is significant at the 005 level (2-tailed)

76 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

against the physical attributes of the noise signals in-cluding background noise level tone frequency andthe five levels of tone strengths The repeated measureANOVA (analysis of variance) confirms that there wereno statistically significant differences between task per-formances across the various noise attributes Thussubjects did not complete more puzzles or have higheraccuracy under any particular tonal frequency back-ground noise level or tone strength although there

appears to be a slight tendency of lower accuracy withgreater tone strength

32 Relating Noise Attributes toAnnoyance Responses

To understand how the physical aspects of the noisesignals (background noise level tone frequency andtonal strength) related to annoyance a three-way repeated

Fig 5mdashAveraged accuracy of Sudoku puzzle answers as task performance scores plotted against(a) Background noise level (b) Tonal frequency and (c) Strength of the tones where Tone1 indicates the least prominent tone and Tone 5 indicates the most prominent tone Errorbars indicate one standard error

Fig 6mdashAveraged number of completed Sudoku puzzles as task performance scores plotted against(a) Background noise level (b) Tonal frequency and (c) Strength of the tones where Tone1 indicates the least prominent tone and Tone 5 indicates the most prominent tone Errorbars indicate one standard error

77Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

measure ANOVAwas conducted Mauchlys test indicatedthat the assumption of sphericity had been met for themain effects of tonal strength and its interactions withtone frequency and background noise level The analysisshows a significant main effect of background noise level[F(17) = 8261 p lt 0001 2p = 092] tone frequency

[F(17) = 2001 p = 0003 2p = 074] and tonal strength

[F(428) = 476 p = 0005 2p = 041] on annoyanceThe main analysis shows that the 55 dBA based

tonal signals were significantly more annoying than40 dBA based tonal signals and that the 125 Hz tonalsignals were significantly more annoying than 500 Hztonal signals Contrast comparisons reveal that the 4thhighest [F(17) = 10420 p = 0014] and 5th highest[F(17) = 12069 p = 0010] in prominence tonal signalswere perceived as more annoying than the least (1st)prominent tonal signals

Figure 7 illustrates the mean annoyance ratings acrossbackground noise levels tonal frequencies and tonestrengths Summarizing these results the overall back-ground noise level does impact annoyance with higherlevels leading to greater annoyance The lower fre-quency tone generated greater annoyance ratings butone should note that the prominence levels of the125 Hz tone versus those of the 500 Hz tone used inthe study were not the same even though the relativedifferences from the threshold of tones presented inISO 1996-22007 are the same There was also a sig-nificant interaction effect between background noiselevel and tone frequency [F(17) = 3331 p = 0014

2p = 060] As plotted in Fig 7(d) the difference be-tween annoyance ratings of the 125 and 500 Hz toneswas greater with the 40 dBA background noise levelcondition than with the 55 dBA background noiselevel condition It appears that tonal frequency is lessrelated to annoyance at higher background noiselevels but plays a larger role at lower backgroundnoise levels

The data on tonal strength shows that higher tone levelsare linked to higher annoyance ratings analysis of the datato determine a threshold of annoyance is presented inFrancis et als study33 Noise sensitivity was expected tobe associated with annoyance but did not demonstrate sta-tistically significant effects in the ANOVA analysis as abetween-subjects factor This is attributed to the limitednumber of subjects in the study which was selected basedon a power analysis of previous annoyance results ratherthan noise sensitivity results

33 Correlations of Noise Metrics withSubjective Responses

The previous section showed that physical aspects ofthe noise signals (specifically loudness and tonality) werecorrelated with annoyance responses in this sectionassorted metrics for quantifying those physical aspectsare tested against the subjective responses Spearmansnonparametric correlation coefficients were calculatedbetween a number of noise metrics and the average par-ticipants perception ratings of loudness annoyance andTLX-avg The results have been analyzed in two ways

Fig 7mdashMean annoyance perception ratings plotted against (a) Background noise level (b) Tonalfrequency (c) Strength of the tones where Tone 1 indicates the least prominent tone andTone 5 indicates the most prominent tone and (d) Interaction of background noise leveland tonal frequency Error bars indicate one standard error

78 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

first with all twenty tonal signals included and then withthe average ratings for ten signals grouped separately bythe broadband background noise level (40 or 55 dBA)Table 4 presents correlation coefficients between all noisemetrics with the subjective perception responses

When analyzing all signals the noise metric thatdemonstrates the highest correlation coefficients withthe perceived loudness annoyance and TLX-avg rat-ings is ANSI loudness level Other loudness metricswere also significantly correlated to the perception rat-ings but the tonality metrics such as prominence ratiotone-to-noise ratio tonality audibility and Aures tonal-ity did not statistically correlate or had lower coefficientsthan loudness metrics This confirms that loudness is themost dominant factor in determining subjective percep-tion of noise

When the signals are grouped separately by broadbandbackground noise levels though tonality metrics didshow higher correlations with subjective ratings thanloudness metrics The coefficient values for the assortedtonality metrics are all very similar with no particularmetric clearly performing better than others Howeverwhen only looking for correlation coefficients with an-noyance tonal audibility showed slightly higher corre-lation coefficients than other tonality metrics (0888for 40 dBA BNL and 0891 for 55 dBA BNL) Aurestonality also showed high correlation with annoyancefrom 55 dBA BNL signals (0903) but it showed lowercorrelation than other metrics with 40 dBA BNL sig-nals (0709) The results indicate that when the broad-band background noise level is controlled or comparabletonality becomes a more influencing factor on annoyanceevaluation Figure 8 presents scatterplots of the averagedannoyance responses (a) with the ANSI loudness levelacross the entire group and (b) with tonal audibility sepa-rated by background noise level

For all cases combined metrics such as the JointNordic Method tone-corrected perceived noise leveland sound quality indicator did not show remarkablybetter performance than loudness metrics even thoughthese combined metrics were significantly related withannoyance ratings The results suggest that imposingpenalty values to loudness levels based on tonal strengthmay not be the most appropriate way to quantify overallsubjective annoyance of tonal noise Instead using sepa-rate metrics to account for tonality and loudness of build-ing mechanical noises is an effective way to relate to thesignals annoyance

34 Regression Model between Noise Metricsand Annoyance

Based on the results in Table 4 ANSI loudness leveland tonal audibility were selected to be used as predictors

Table 4mdashSpearmans correlation analysis of noisemetrics against subjective responses andSudoku puzzle task performance The resultsare analyzed first with all signals includedand then in two groups separated by back-ground noise level (40 or 55 dBA) Boldedvalues indicate metrics chosen for use inthe regression model based on their overallhigh significant correlation values

All signals (40 dBA and 55 dBA BNL)

Loudness Annoyance TLX-avgPR 0150 0186 0147TNR 0123 0081 0095ΔLta 0006 0056 0019Aures 0297 0359 0314dB 0805 0824 0772dBA 0866 0887 0842ANSI loudness 0946 0950 0926ISO loudness 0938 0952 0925PNL 0892 0920 0886PNLT 0869 0877 0826JNM 0840 0869 0818SQI 0904 0899 0856

40 dBA BNL only

PR 0794 0867 0782TNR 0794 0867 0782ΔLta 0778 0888 0815Aures 0673 0709 0697dB 0806 0939 0855dBA 0794 0927 0830ANSI loudness 0685 0745 0697ISO loudness 0685 0745 0697PNL 0685 0842 0867PNLT 0794 0830 0758JNM 0794 0927 0830SQI 0806 0806 0709

55 dBA BNL only

PR 0799 0867 0758TNR 0709 0845 0845ΔLta 0787 0891 0818Aures 0781 0903 0782dB 0715 0756 0530dBA 0707 0770 0564ANSI loudness 0878 0855 0709ISO loudness 0817 0867 0697PNL 0720 0806 0539PNLT 0744 0782 0527JNM 0707 0770 0564SQI 0689 0663 0444

Correlation is significant at the 001 level (2-tailed)Correlation is significant at the 005 level (2-tailed)

79Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

for a linear multiple regression model for annoyancebecause these two metrics resulted in among the stron-gest correlations with annoyance perception compared

to other noise metrics Equation (2) presents the multi-variate regression model with ANSI loudness level andtonal audibility

Annoyance frac14 1806 thorn 1164 ANSI Loudness soneeth THORNfrac12 thorn 0072 Tonal Audibility dBeth THORNfrac12

eth2THORN

Table 5 also presents standard error of coefficientsstandardized coefficients and statistical significance whenANSI loudness level was only used (in step 1) and whentonal audibility was also included (in step 2) in additionto the coefficient values for each predictor Standardizedb values indicate the number of standard deviations thatthe outcome annoyance will change as a result of onestandard deviation change in the predictor The R2 valuefor the first step model is 0943 which is a measure ofgoodness-of-fit of linear regression indicating that 943of the annoyance rating variance can be explained by theANSI loudness model only When including tonal audibil-ity as a second predictor the R2 value increased to 0962Even though this increase is small the multivariate re-gression model does significantly predict more variationin annoyance perception when including tonal audibilityas a second predictor for step 2 the ANSI loudnesslevel [t(17) = 20796 p lt 0001] and tonal audibility[t(17) = 2943 p = 0009] are both significant predictorsof annoyance Figure 9 illustrates a regression line withthe calculated linear model

The results of the correlation analysis and regressionmodel presented in this paper are in line with the find-ings from More and Daviesrsquo study8 which focused onaircraft flyover noise rather than building mechanicalsystem noise Their work focused only on annoyanceand used metric values that were exceeded some per-centage (often 5) of the time since their flyover

Fig 8mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals against (a) ANSIloudness level for all signals and(b) Tonal audibility for 40 and 55 dBABNL separately Dashed lines indicateregression lines of annoyance ratingprediction with regard to each metric

Table 5mdashLinear regression model of predictors for annoyance perception with 95 bias corrected and accel-erated confidence intervals reported in parentheses Confidence intervals and standard errors arebased on 1000 bootstrap samples Standardized b values indicate the number of standard deviationsthat the outcome annoyance will change as a result of one standard deviation change in the predictor

b Standarderror B

b p

Step 1Constant 3254(2305 4310) 0512 p = 0001ANSI loudness (sone) 1137(1004 1263) 0066 0971 p = 0001

Step 2Constant 1806(0498 3187) 0683 p = 0020ANSI loudness (sone) 1164(1043 1308) 0069 0994 p = 0001Tonal audibility (dB) 0072(0027 0111) 0021 0141 p = 0004

Note 0943 for Step 1 ΔR2 = 0019 for Step 2

80 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

signals varied in time They demonstrated that the bestregression model when using existing metrics to matchtheir annoyance responses included both a loudnessmetric and a tonality metric and they proposed revi-sion to the penalty values used for the Joint NordicMethod to improve its individual predictive capabilityThe authors feel though that it is not necessary tomodify a combined metric (like Joint Nordic Method)since using individual metrics for loudness and for to-nality in the proposed regression model herein demon-strated high correlations to the annoyance responseson their own

4 SUMMARYAND CONCLUSION

The purpose of this study was to investigate hownoise signals with varying degrees of prominent tonessimilar to those produced by building mechanicalequipment affect subjective annoyance perception andtask performance and to develop a prediction modelof annoyance using current noise metrics Subjectscompleted Sudoku puzzles and a questionnaire modi-fied from the NASA task load index to quantify theoverall workload caused by building mechanical noisein this study No statistically significant effect was foundbetween the tonal signals used in this study and task per-formance although there was a trend of decreasing accu-racy with increasing tone strengths based on correctSudoku puzzle answers The validity of the modified taskload index questionnaire was high based on its reliabilitycoefficient and testndashretest coefficient and the average re-sponse from the questionnaire was found to significantlycorrelate with perceived annoyance and loudness of thebackground noise signals A factorial repeated measure

ANOVA revealed that participants felt more annoyed withincreasing background noise level lower tone frequencyand higher tone strength Correlation analysis with noisemetrics and subjective perception ratings found that ANSIloudness level among all other loudness metrics correlatesmost strongly with annoyance perception while assortedtonality metrics showed relatively weaker but still statisti-cally significant correlations with annoyance A statisticallysignificant multivariate regression model with ANSI loud-ness level and tonal audibility has been developed whichdemonstrates an R2 value of 0962

While noise sensitivity of test subjects was surveyedno statistically significant relations between perceptionor performance results and noise sensitivity were foundlikely due to the limited number of test subjects Futurework in this area is suggested with more test subjectsand more tonal signals to understand better the roleof noise sensitivity Also tonal noises from actual buildingmechanical systems often demonstrate multiple tones whichmay be inharmonic or which can fluctuate in time addi-tional investigations using tonal signals that incorporatethese other factors are recommended

5 ACKNOWLEDGMENTS

Many thanks to the undergraduate research assis-tants Adam Steinbach and Kristin Hanna who assistedwith the study design subjective testing and data analysisPartial funding for this project came from an Institute ofNoise Control Engineering Undergraduate Research Grant

6 REFERENCES

1 International Organization for Standardization (ISO) ldquoAcousticsmdashDescription Measurement and Assessment of EnvironmentalNoisemdashPart 2 Determination of Environmental Noise LevelsrdquoInternational Standard ISO 1996ndash22007 International Organi-zation for Standardization (2007)

2 ldquoSeattle Municipal Code Chapter 2508 Noise Controlrdquo Seattle(2007)

3 ldquoNoise Control Ordinance of the County of Los Angelesrdquo LosAngeles County (1978)

4 ldquoOrdinance of Minnesota Chapter 389 ndash Noiserdquo Minnesota(2008)

5 NY ADC LAW 27-770 NY code ndash Section 27-770 ldquoNoiseControl of Mechanical Equipmentrdquo New York (2006)

6 RP Hellman ldquoGrowth rate of loudness annoyance and noisi-ness as a function of tone location within the noise spectrumrdquoJ Acoust Soc Am 75(1) 209ndash218 (1984)

7 RP Hellman ldquoPerceived magnitude of two-tone-noise com-plexes loudness annoyance and noisinessrdquo J Acoust SocAm 77(4) 1497ndash1504 (1985)

8 S More and P Davies ldquoHuman responses to the tonalness of air-craft noiserdquo Noise Control Engr J 58(4) 420ndash440 (2010)

9 KH Lee P Davies and AM Surprenant ldquoQuantification ofthe tonal prominence of complex tones in machinery noiserdquoNoiseCon04 (2004)

Fig 9mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals plotted against theproposed linear regression model ofannoyance perception (dashed) basedon ANSI loudness level and tonalaudibility (R2 = 096)

81Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

10 A Hastings H Lee P Davies and AM Surprenant ldquoMeasure-ment of the attributes of complex tonal components commonlyfound in product soundrdquo Noise Control Engr J 51(4) 195ndash209(2003)

11 EE Ryherd and LM Wang ldquoImplications of human perfor-mance and perception under tonal noise conditions on indoornoise criteriardquo J Acoust Soc Am 124(1) 218ndash226 (2008)

12 P Susini S McAdams S Winsberg I Perry S Vieillard andX Rodet ldquoCharacterizing the sound quality of air-conditioningnoiserdquo Appl Acoust 65(8) 763ndash790 (2004)

13 B Berglund P Hassmeacuten and A Preis ldquoAnnoyance and spectralcontrast are cues for similarity and preference of soundsrdquo J SoundVibr 250(1) 53ndash64 (2002)

14 U Landstroumlm E Aringkerlund A Kjellberg and M Tesarz ldquoEx-posure levels tonal components and noise annoyance in work-ing environmentsrdquo Environ Int 21(3) 265ndash275 (1995)

15 U Landstroumlm A Kjellberg and M Bystroumlm ldquoAcceptablelevels of sounds with different spectral characteristics duringthe performance of a simple and a complex non-auditory taskrdquoJ Sound Vibr 160(3) 533ndash542 (1993)

16 D Laird ldquoThe influence of noise on production and fatigue asrelated to pitch sensation level and steadiness of the noiserdquo JAppl Psychol 17(3) 320ndash330 (1933)

17 JV Grjmaldi ldquoSensori-motor performance under varying noiseconditionsrdquo Ergonomics 2(1) 34ndash43 (1958)

18 K Holmberg U Landstrom and A Kjellberg ldquoEffects of ven-tilation noise due to frequency characteristic and sound levelrdquoJ Low Freq Noise Vib 12(4) 115ndash122 (1993)

19 International Organization for Standardization (ISO) ldquoAcousticsmdashAssessment of noise annoyance by means of social and socio-acoustic surveysrdquo International Standard ISOTS 15666 2003International Organization for Standardization (2003)

20 TH Pedersen ldquoThe ldquoGenlydrdquo noise annoyance model dose-response relationships modelled by logistic functionsrdquo DELTAHoslashrsholm Danmark (2007)

21 CMarquis-Favre E Premat andDAubreacutee ldquoNoise and its effectsmdasha review on qualitative aspects of sound Part II noise and annoy-ancerdquo Acta Acust united with Acust 91(4) 626ndash642 (2005)

22 American National Standards Institute (ANSI) ldquoAcousticsmdashMeasurement of Airborne Noise Emitted by Information Technol-ogy and Telecommunications EquipmentmdashPart 1 Determinationof Sound Power Level and Emission Sound Pressure LevelrdquoAmerican National Standards Institute ANSIASA S1210-2010Part 1 American Society of America (2010)

23 W Aures ldquoThe sensory euphony as a function of auditory sen-sationsrdquo Acustica 58(5) 282ndash290 (1985)

24 American National Standards Institute (ANSI) ldquoProcedure forthe Computation of Loudness of Steady Soundsrdquo AmericanNational Standards Institute ANSI S34-2007 Acoustical Soci-ety of America (2007)

25 ldquoAcousticsmdashMethod for Calculating Loudness Levelrdquo Interna-tional Standard ISO 5321975 International Organization forStandardization (1975)

26 E Zwicker ldquoSubdivision of the audible frequency range intocritical bands (Frequenzgruppen)rdquo J Acoust Soc Am 33(2)248ndash248 (1961)

27 BR Glasberg and BCJ Moore ldquoPrediction of absolutethresholds and equal loudness contours using a modified loud-ness modelrdquo J Acoust Soc Am 120 585ndash588 (2006)

28 Federal Aviation Administration ldquoPart 36 Noise StandardsAircraft Type and Airworthiness Certificationrdquo Federal Avia-tion Regulations Federal Aviation Administration (1969)

29 Air-Conditioning Heating and Refrigeration Institute (AHRI)ldquoSound Quality Evaluation Procedures for Air-Conditioningand Refrigeration Equipmentrdquo Air-Conditioning Heating andRefrigeration Institute AHRIANSI 1140-2012 Air-ConditioningHeating and Refrigeration Institute (2012)

30 WE Blazier ldquoRevised noise criteria for application in theacoustical design and rating of HVAC systemsrdquo Noise ControlEngr 16(2) 64ndash73 (1981)

31 F Faul E Erdfelder A Buchner and A-G Lang ldquoStatisticalpower analyses using GPower 31 tests for correlation and re-gression analysesrdquo Behav Res Methods 41 1149ndash1160 (2009)

32 M Schutte A Marks E Wenning and B Griefahn ldquoThe de-velopment of the noise sensitivity questionnairerdquo Noise Heal9(34) 15 (2007)

33 J Francis J Lee A Steinbach and LM Wang ldquoDeterminingannoyance thresholds of tones in noiserdquo ASHRAE Trans120 (2014)

34 JW Grabbe ldquoSudoku and working memory performance forolder adultsrdquo Act Adapt Aging 35(3) 241ndash254 (2011)

35 HS Chang and JM Gibson ldquoThe oddndasheven effect in Sudokupuzzles effects of working memory aging and experiencerdquoAm J Psychol 124(3) 313ndash324 (2011)

36 ldquoNASA TLX Task Load Indexrdquo [Online] Available at httphumansystemsarcnasagovgroupstlx (accessed September 92015)

37 RFS Job J Hatfield NL Carter P Peploe R Taylor andS Morrell ldquoGeneral scales of community reaction to noise(dissatisfaction and perceived affectedness) are more reliablethan scales of annoyancerdquo J Acoust Soc Am 110(2) 939ndash946(2001)

82 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

against the physical attributes of the noise signals in-cluding background noise level tone frequency andthe five levels of tone strengths The repeated measureANOVA (analysis of variance) confirms that there wereno statistically significant differences between task per-formances across the various noise attributes Thussubjects did not complete more puzzles or have higheraccuracy under any particular tonal frequency back-ground noise level or tone strength although there

appears to be a slight tendency of lower accuracy withgreater tone strength

32 Relating Noise Attributes toAnnoyance Responses

To understand how the physical aspects of the noisesignals (background noise level tone frequency andtonal strength) related to annoyance a three-way repeated

Fig 5mdashAveraged accuracy of Sudoku puzzle answers as task performance scores plotted against(a) Background noise level (b) Tonal frequency and (c) Strength of the tones where Tone1 indicates the least prominent tone and Tone 5 indicates the most prominent tone Errorbars indicate one standard error

Fig 6mdashAveraged number of completed Sudoku puzzles as task performance scores plotted against(a) Background noise level (b) Tonal frequency and (c) Strength of the tones where Tone1 indicates the least prominent tone and Tone 5 indicates the most prominent tone Errorbars indicate one standard error

77Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

measure ANOVAwas conducted Mauchlys test indicatedthat the assumption of sphericity had been met for themain effects of tonal strength and its interactions withtone frequency and background noise level The analysisshows a significant main effect of background noise level[F(17) = 8261 p lt 0001 2p = 092] tone frequency

[F(17) = 2001 p = 0003 2p = 074] and tonal strength

[F(428) = 476 p = 0005 2p = 041] on annoyanceThe main analysis shows that the 55 dBA based

tonal signals were significantly more annoying than40 dBA based tonal signals and that the 125 Hz tonalsignals were significantly more annoying than 500 Hztonal signals Contrast comparisons reveal that the 4thhighest [F(17) = 10420 p = 0014] and 5th highest[F(17) = 12069 p = 0010] in prominence tonal signalswere perceived as more annoying than the least (1st)prominent tonal signals

Figure 7 illustrates the mean annoyance ratings acrossbackground noise levels tonal frequencies and tonestrengths Summarizing these results the overall back-ground noise level does impact annoyance with higherlevels leading to greater annoyance The lower fre-quency tone generated greater annoyance ratings butone should note that the prominence levels of the125 Hz tone versus those of the 500 Hz tone used inthe study were not the same even though the relativedifferences from the threshold of tones presented inISO 1996-22007 are the same There was also a sig-nificant interaction effect between background noiselevel and tone frequency [F(17) = 3331 p = 0014

2p = 060] As plotted in Fig 7(d) the difference be-tween annoyance ratings of the 125 and 500 Hz toneswas greater with the 40 dBA background noise levelcondition than with the 55 dBA background noiselevel condition It appears that tonal frequency is lessrelated to annoyance at higher background noiselevels but plays a larger role at lower backgroundnoise levels

The data on tonal strength shows that higher tone levelsare linked to higher annoyance ratings analysis of the datato determine a threshold of annoyance is presented inFrancis et als study33 Noise sensitivity was expected tobe associated with annoyance but did not demonstrate sta-tistically significant effects in the ANOVA analysis as abetween-subjects factor This is attributed to the limitednumber of subjects in the study which was selected basedon a power analysis of previous annoyance results ratherthan noise sensitivity results

33 Correlations of Noise Metrics withSubjective Responses

The previous section showed that physical aspects ofthe noise signals (specifically loudness and tonality) werecorrelated with annoyance responses in this sectionassorted metrics for quantifying those physical aspectsare tested against the subjective responses Spearmansnonparametric correlation coefficients were calculatedbetween a number of noise metrics and the average par-ticipants perception ratings of loudness annoyance andTLX-avg The results have been analyzed in two ways

Fig 7mdashMean annoyance perception ratings plotted against (a) Background noise level (b) Tonalfrequency (c) Strength of the tones where Tone 1 indicates the least prominent tone andTone 5 indicates the most prominent tone and (d) Interaction of background noise leveland tonal frequency Error bars indicate one standard error

78 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

first with all twenty tonal signals included and then withthe average ratings for ten signals grouped separately bythe broadband background noise level (40 or 55 dBA)Table 4 presents correlation coefficients between all noisemetrics with the subjective perception responses

When analyzing all signals the noise metric thatdemonstrates the highest correlation coefficients withthe perceived loudness annoyance and TLX-avg rat-ings is ANSI loudness level Other loudness metricswere also significantly correlated to the perception rat-ings but the tonality metrics such as prominence ratiotone-to-noise ratio tonality audibility and Aures tonal-ity did not statistically correlate or had lower coefficientsthan loudness metrics This confirms that loudness is themost dominant factor in determining subjective percep-tion of noise

When the signals are grouped separately by broadbandbackground noise levels though tonality metrics didshow higher correlations with subjective ratings thanloudness metrics The coefficient values for the assortedtonality metrics are all very similar with no particularmetric clearly performing better than others Howeverwhen only looking for correlation coefficients with an-noyance tonal audibility showed slightly higher corre-lation coefficients than other tonality metrics (0888for 40 dBA BNL and 0891 for 55 dBA BNL) Aurestonality also showed high correlation with annoyancefrom 55 dBA BNL signals (0903) but it showed lowercorrelation than other metrics with 40 dBA BNL sig-nals (0709) The results indicate that when the broad-band background noise level is controlled or comparabletonality becomes a more influencing factor on annoyanceevaluation Figure 8 presents scatterplots of the averagedannoyance responses (a) with the ANSI loudness levelacross the entire group and (b) with tonal audibility sepa-rated by background noise level

For all cases combined metrics such as the JointNordic Method tone-corrected perceived noise leveland sound quality indicator did not show remarkablybetter performance than loudness metrics even thoughthese combined metrics were significantly related withannoyance ratings The results suggest that imposingpenalty values to loudness levels based on tonal strengthmay not be the most appropriate way to quantify overallsubjective annoyance of tonal noise Instead using sepa-rate metrics to account for tonality and loudness of build-ing mechanical noises is an effective way to relate to thesignals annoyance

34 Regression Model between Noise Metricsand Annoyance

Based on the results in Table 4 ANSI loudness leveland tonal audibility were selected to be used as predictors

Table 4mdashSpearmans correlation analysis of noisemetrics against subjective responses andSudoku puzzle task performance The resultsare analyzed first with all signals includedand then in two groups separated by back-ground noise level (40 or 55 dBA) Boldedvalues indicate metrics chosen for use inthe regression model based on their overallhigh significant correlation values

All signals (40 dBA and 55 dBA BNL)

Loudness Annoyance TLX-avgPR 0150 0186 0147TNR 0123 0081 0095ΔLta 0006 0056 0019Aures 0297 0359 0314dB 0805 0824 0772dBA 0866 0887 0842ANSI loudness 0946 0950 0926ISO loudness 0938 0952 0925PNL 0892 0920 0886PNLT 0869 0877 0826JNM 0840 0869 0818SQI 0904 0899 0856

40 dBA BNL only

PR 0794 0867 0782TNR 0794 0867 0782ΔLta 0778 0888 0815Aures 0673 0709 0697dB 0806 0939 0855dBA 0794 0927 0830ANSI loudness 0685 0745 0697ISO loudness 0685 0745 0697PNL 0685 0842 0867PNLT 0794 0830 0758JNM 0794 0927 0830SQI 0806 0806 0709

55 dBA BNL only

PR 0799 0867 0758TNR 0709 0845 0845ΔLta 0787 0891 0818Aures 0781 0903 0782dB 0715 0756 0530dBA 0707 0770 0564ANSI loudness 0878 0855 0709ISO loudness 0817 0867 0697PNL 0720 0806 0539PNLT 0744 0782 0527JNM 0707 0770 0564SQI 0689 0663 0444

Correlation is significant at the 001 level (2-tailed)Correlation is significant at the 005 level (2-tailed)

79Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

for a linear multiple regression model for annoyancebecause these two metrics resulted in among the stron-gest correlations with annoyance perception compared

to other noise metrics Equation (2) presents the multi-variate regression model with ANSI loudness level andtonal audibility

Annoyance frac14 1806 thorn 1164 ANSI Loudness soneeth THORNfrac12 thorn 0072 Tonal Audibility dBeth THORNfrac12

eth2THORN

Table 5 also presents standard error of coefficientsstandardized coefficients and statistical significance whenANSI loudness level was only used (in step 1) and whentonal audibility was also included (in step 2) in additionto the coefficient values for each predictor Standardizedb values indicate the number of standard deviations thatthe outcome annoyance will change as a result of onestandard deviation change in the predictor The R2 valuefor the first step model is 0943 which is a measure ofgoodness-of-fit of linear regression indicating that 943of the annoyance rating variance can be explained by theANSI loudness model only When including tonal audibil-ity as a second predictor the R2 value increased to 0962Even though this increase is small the multivariate re-gression model does significantly predict more variationin annoyance perception when including tonal audibilityas a second predictor for step 2 the ANSI loudnesslevel [t(17) = 20796 p lt 0001] and tonal audibility[t(17) = 2943 p = 0009] are both significant predictorsof annoyance Figure 9 illustrates a regression line withthe calculated linear model

The results of the correlation analysis and regressionmodel presented in this paper are in line with the find-ings from More and Daviesrsquo study8 which focused onaircraft flyover noise rather than building mechanicalsystem noise Their work focused only on annoyanceand used metric values that were exceeded some per-centage (often 5) of the time since their flyover

Fig 8mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals against (a) ANSIloudness level for all signals and(b) Tonal audibility for 40 and 55 dBABNL separately Dashed lines indicateregression lines of annoyance ratingprediction with regard to each metric

Table 5mdashLinear regression model of predictors for annoyance perception with 95 bias corrected and accel-erated confidence intervals reported in parentheses Confidence intervals and standard errors arebased on 1000 bootstrap samples Standardized b values indicate the number of standard deviationsthat the outcome annoyance will change as a result of one standard deviation change in the predictor

b Standarderror B

b p

Step 1Constant 3254(2305 4310) 0512 p = 0001ANSI loudness (sone) 1137(1004 1263) 0066 0971 p = 0001

Step 2Constant 1806(0498 3187) 0683 p = 0020ANSI loudness (sone) 1164(1043 1308) 0069 0994 p = 0001Tonal audibility (dB) 0072(0027 0111) 0021 0141 p = 0004

Note 0943 for Step 1 ΔR2 = 0019 for Step 2

80 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

signals varied in time They demonstrated that the bestregression model when using existing metrics to matchtheir annoyance responses included both a loudnessmetric and a tonality metric and they proposed revi-sion to the penalty values used for the Joint NordicMethod to improve its individual predictive capabilityThe authors feel though that it is not necessary tomodify a combined metric (like Joint Nordic Method)since using individual metrics for loudness and for to-nality in the proposed regression model herein demon-strated high correlations to the annoyance responseson their own

4 SUMMARYAND CONCLUSION

The purpose of this study was to investigate hownoise signals with varying degrees of prominent tonessimilar to those produced by building mechanicalequipment affect subjective annoyance perception andtask performance and to develop a prediction modelof annoyance using current noise metrics Subjectscompleted Sudoku puzzles and a questionnaire modi-fied from the NASA task load index to quantify theoverall workload caused by building mechanical noisein this study No statistically significant effect was foundbetween the tonal signals used in this study and task per-formance although there was a trend of decreasing accu-racy with increasing tone strengths based on correctSudoku puzzle answers The validity of the modified taskload index questionnaire was high based on its reliabilitycoefficient and testndashretest coefficient and the average re-sponse from the questionnaire was found to significantlycorrelate with perceived annoyance and loudness of thebackground noise signals A factorial repeated measure

ANOVA revealed that participants felt more annoyed withincreasing background noise level lower tone frequencyand higher tone strength Correlation analysis with noisemetrics and subjective perception ratings found that ANSIloudness level among all other loudness metrics correlatesmost strongly with annoyance perception while assortedtonality metrics showed relatively weaker but still statisti-cally significant correlations with annoyance A statisticallysignificant multivariate regression model with ANSI loud-ness level and tonal audibility has been developed whichdemonstrates an R2 value of 0962

While noise sensitivity of test subjects was surveyedno statistically significant relations between perceptionor performance results and noise sensitivity were foundlikely due to the limited number of test subjects Futurework in this area is suggested with more test subjectsand more tonal signals to understand better the roleof noise sensitivity Also tonal noises from actual buildingmechanical systems often demonstrate multiple tones whichmay be inharmonic or which can fluctuate in time addi-tional investigations using tonal signals that incorporatethese other factors are recommended

5 ACKNOWLEDGMENTS

Many thanks to the undergraduate research assis-tants Adam Steinbach and Kristin Hanna who assistedwith the study design subjective testing and data analysisPartial funding for this project came from an Institute ofNoise Control Engineering Undergraduate Research Grant

6 REFERENCES

1 International Organization for Standardization (ISO) ldquoAcousticsmdashDescription Measurement and Assessment of EnvironmentalNoisemdashPart 2 Determination of Environmental Noise LevelsrdquoInternational Standard ISO 1996ndash22007 International Organi-zation for Standardization (2007)

2 ldquoSeattle Municipal Code Chapter 2508 Noise Controlrdquo Seattle(2007)

3 ldquoNoise Control Ordinance of the County of Los Angelesrdquo LosAngeles County (1978)

4 ldquoOrdinance of Minnesota Chapter 389 ndash Noiserdquo Minnesota(2008)

5 NY ADC LAW 27-770 NY code ndash Section 27-770 ldquoNoiseControl of Mechanical Equipmentrdquo New York (2006)

6 RP Hellman ldquoGrowth rate of loudness annoyance and noisi-ness as a function of tone location within the noise spectrumrdquoJ Acoust Soc Am 75(1) 209ndash218 (1984)

7 RP Hellman ldquoPerceived magnitude of two-tone-noise com-plexes loudness annoyance and noisinessrdquo J Acoust SocAm 77(4) 1497ndash1504 (1985)

8 S More and P Davies ldquoHuman responses to the tonalness of air-craft noiserdquo Noise Control Engr J 58(4) 420ndash440 (2010)

9 KH Lee P Davies and AM Surprenant ldquoQuantification ofthe tonal prominence of complex tones in machinery noiserdquoNoiseCon04 (2004)

Fig 9mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals plotted against theproposed linear regression model ofannoyance perception (dashed) basedon ANSI loudness level and tonalaudibility (R2 = 096)

81Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

10 A Hastings H Lee P Davies and AM Surprenant ldquoMeasure-ment of the attributes of complex tonal components commonlyfound in product soundrdquo Noise Control Engr J 51(4) 195ndash209(2003)

11 EE Ryherd and LM Wang ldquoImplications of human perfor-mance and perception under tonal noise conditions on indoornoise criteriardquo J Acoust Soc Am 124(1) 218ndash226 (2008)

12 P Susini S McAdams S Winsberg I Perry S Vieillard andX Rodet ldquoCharacterizing the sound quality of air-conditioningnoiserdquo Appl Acoust 65(8) 763ndash790 (2004)

13 B Berglund P Hassmeacuten and A Preis ldquoAnnoyance and spectralcontrast are cues for similarity and preference of soundsrdquo J SoundVibr 250(1) 53ndash64 (2002)

14 U Landstroumlm E Aringkerlund A Kjellberg and M Tesarz ldquoEx-posure levels tonal components and noise annoyance in work-ing environmentsrdquo Environ Int 21(3) 265ndash275 (1995)

15 U Landstroumlm A Kjellberg and M Bystroumlm ldquoAcceptablelevels of sounds with different spectral characteristics duringthe performance of a simple and a complex non-auditory taskrdquoJ Sound Vibr 160(3) 533ndash542 (1993)

16 D Laird ldquoThe influence of noise on production and fatigue asrelated to pitch sensation level and steadiness of the noiserdquo JAppl Psychol 17(3) 320ndash330 (1933)

17 JV Grjmaldi ldquoSensori-motor performance under varying noiseconditionsrdquo Ergonomics 2(1) 34ndash43 (1958)

18 K Holmberg U Landstrom and A Kjellberg ldquoEffects of ven-tilation noise due to frequency characteristic and sound levelrdquoJ Low Freq Noise Vib 12(4) 115ndash122 (1993)

19 International Organization for Standardization (ISO) ldquoAcousticsmdashAssessment of noise annoyance by means of social and socio-acoustic surveysrdquo International Standard ISOTS 15666 2003International Organization for Standardization (2003)

20 TH Pedersen ldquoThe ldquoGenlydrdquo noise annoyance model dose-response relationships modelled by logistic functionsrdquo DELTAHoslashrsholm Danmark (2007)

21 CMarquis-Favre E Premat andDAubreacutee ldquoNoise and its effectsmdasha review on qualitative aspects of sound Part II noise and annoy-ancerdquo Acta Acust united with Acust 91(4) 626ndash642 (2005)

22 American National Standards Institute (ANSI) ldquoAcousticsmdashMeasurement of Airborne Noise Emitted by Information Technol-ogy and Telecommunications EquipmentmdashPart 1 Determinationof Sound Power Level and Emission Sound Pressure LevelrdquoAmerican National Standards Institute ANSIASA S1210-2010Part 1 American Society of America (2010)

23 W Aures ldquoThe sensory euphony as a function of auditory sen-sationsrdquo Acustica 58(5) 282ndash290 (1985)

24 American National Standards Institute (ANSI) ldquoProcedure forthe Computation of Loudness of Steady Soundsrdquo AmericanNational Standards Institute ANSI S34-2007 Acoustical Soci-ety of America (2007)

25 ldquoAcousticsmdashMethod for Calculating Loudness Levelrdquo Interna-tional Standard ISO 5321975 International Organization forStandardization (1975)

26 E Zwicker ldquoSubdivision of the audible frequency range intocritical bands (Frequenzgruppen)rdquo J Acoust Soc Am 33(2)248ndash248 (1961)

27 BR Glasberg and BCJ Moore ldquoPrediction of absolutethresholds and equal loudness contours using a modified loud-ness modelrdquo J Acoust Soc Am 120 585ndash588 (2006)

28 Federal Aviation Administration ldquoPart 36 Noise StandardsAircraft Type and Airworthiness Certificationrdquo Federal Avia-tion Regulations Federal Aviation Administration (1969)

29 Air-Conditioning Heating and Refrigeration Institute (AHRI)ldquoSound Quality Evaluation Procedures for Air-Conditioningand Refrigeration Equipmentrdquo Air-Conditioning Heating andRefrigeration Institute AHRIANSI 1140-2012 Air-ConditioningHeating and Refrigeration Institute (2012)

30 WE Blazier ldquoRevised noise criteria for application in theacoustical design and rating of HVAC systemsrdquo Noise ControlEngr 16(2) 64ndash73 (1981)

31 F Faul E Erdfelder A Buchner and A-G Lang ldquoStatisticalpower analyses using GPower 31 tests for correlation and re-gression analysesrdquo Behav Res Methods 41 1149ndash1160 (2009)

32 M Schutte A Marks E Wenning and B Griefahn ldquoThe de-velopment of the noise sensitivity questionnairerdquo Noise Heal9(34) 15 (2007)

33 J Francis J Lee A Steinbach and LM Wang ldquoDeterminingannoyance thresholds of tones in noiserdquo ASHRAE Trans120 (2014)

34 JW Grabbe ldquoSudoku and working memory performance forolder adultsrdquo Act Adapt Aging 35(3) 241ndash254 (2011)

35 HS Chang and JM Gibson ldquoThe oddndasheven effect in Sudokupuzzles effects of working memory aging and experiencerdquoAm J Psychol 124(3) 313ndash324 (2011)

36 ldquoNASA TLX Task Load Indexrdquo [Online] Available at httphumansystemsarcnasagovgroupstlx (accessed September 92015)

37 RFS Job J Hatfield NL Carter P Peploe R Taylor andS Morrell ldquoGeneral scales of community reaction to noise(dissatisfaction and perceived affectedness) are more reliablethan scales of annoyancerdquo J Acoust Soc Am 110(2) 939ndash946(2001)

82 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

measure ANOVAwas conducted Mauchlys test indicatedthat the assumption of sphericity had been met for themain effects of tonal strength and its interactions withtone frequency and background noise level The analysisshows a significant main effect of background noise level[F(17) = 8261 p lt 0001 2p = 092] tone frequency

[F(17) = 2001 p = 0003 2p = 074] and tonal strength

[F(428) = 476 p = 0005 2p = 041] on annoyanceThe main analysis shows that the 55 dBA based

tonal signals were significantly more annoying than40 dBA based tonal signals and that the 125 Hz tonalsignals were significantly more annoying than 500 Hztonal signals Contrast comparisons reveal that the 4thhighest [F(17) = 10420 p = 0014] and 5th highest[F(17) = 12069 p = 0010] in prominence tonal signalswere perceived as more annoying than the least (1st)prominent tonal signals

Figure 7 illustrates the mean annoyance ratings acrossbackground noise levels tonal frequencies and tonestrengths Summarizing these results the overall back-ground noise level does impact annoyance with higherlevels leading to greater annoyance The lower fre-quency tone generated greater annoyance ratings butone should note that the prominence levels of the125 Hz tone versus those of the 500 Hz tone used inthe study were not the same even though the relativedifferences from the threshold of tones presented inISO 1996-22007 are the same There was also a sig-nificant interaction effect between background noiselevel and tone frequency [F(17) = 3331 p = 0014

2p = 060] As plotted in Fig 7(d) the difference be-tween annoyance ratings of the 125 and 500 Hz toneswas greater with the 40 dBA background noise levelcondition than with the 55 dBA background noiselevel condition It appears that tonal frequency is lessrelated to annoyance at higher background noiselevels but plays a larger role at lower backgroundnoise levels

The data on tonal strength shows that higher tone levelsare linked to higher annoyance ratings analysis of the datato determine a threshold of annoyance is presented inFrancis et als study33 Noise sensitivity was expected tobe associated with annoyance but did not demonstrate sta-tistically significant effects in the ANOVA analysis as abetween-subjects factor This is attributed to the limitednumber of subjects in the study which was selected basedon a power analysis of previous annoyance results ratherthan noise sensitivity results

33 Correlations of Noise Metrics withSubjective Responses

The previous section showed that physical aspects ofthe noise signals (specifically loudness and tonality) werecorrelated with annoyance responses in this sectionassorted metrics for quantifying those physical aspectsare tested against the subjective responses Spearmansnonparametric correlation coefficients were calculatedbetween a number of noise metrics and the average par-ticipants perception ratings of loudness annoyance andTLX-avg The results have been analyzed in two ways

Fig 7mdashMean annoyance perception ratings plotted against (a) Background noise level (b) Tonalfrequency (c) Strength of the tones where Tone 1 indicates the least prominent tone andTone 5 indicates the most prominent tone and (d) Interaction of background noise leveland tonal frequency Error bars indicate one standard error

78 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

first with all twenty tonal signals included and then withthe average ratings for ten signals grouped separately bythe broadband background noise level (40 or 55 dBA)Table 4 presents correlation coefficients between all noisemetrics with the subjective perception responses

When analyzing all signals the noise metric thatdemonstrates the highest correlation coefficients withthe perceived loudness annoyance and TLX-avg rat-ings is ANSI loudness level Other loudness metricswere also significantly correlated to the perception rat-ings but the tonality metrics such as prominence ratiotone-to-noise ratio tonality audibility and Aures tonal-ity did not statistically correlate or had lower coefficientsthan loudness metrics This confirms that loudness is themost dominant factor in determining subjective percep-tion of noise

When the signals are grouped separately by broadbandbackground noise levels though tonality metrics didshow higher correlations with subjective ratings thanloudness metrics The coefficient values for the assortedtonality metrics are all very similar with no particularmetric clearly performing better than others Howeverwhen only looking for correlation coefficients with an-noyance tonal audibility showed slightly higher corre-lation coefficients than other tonality metrics (0888for 40 dBA BNL and 0891 for 55 dBA BNL) Aurestonality also showed high correlation with annoyancefrom 55 dBA BNL signals (0903) but it showed lowercorrelation than other metrics with 40 dBA BNL sig-nals (0709) The results indicate that when the broad-band background noise level is controlled or comparabletonality becomes a more influencing factor on annoyanceevaluation Figure 8 presents scatterplots of the averagedannoyance responses (a) with the ANSI loudness levelacross the entire group and (b) with tonal audibility sepa-rated by background noise level

For all cases combined metrics such as the JointNordic Method tone-corrected perceived noise leveland sound quality indicator did not show remarkablybetter performance than loudness metrics even thoughthese combined metrics were significantly related withannoyance ratings The results suggest that imposingpenalty values to loudness levels based on tonal strengthmay not be the most appropriate way to quantify overallsubjective annoyance of tonal noise Instead using sepa-rate metrics to account for tonality and loudness of build-ing mechanical noises is an effective way to relate to thesignals annoyance

34 Regression Model between Noise Metricsand Annoyance

Based on the results in Table 4 ANSI loudness leveland tonal audibility were selected to be used as predictors

Table 4mdashSpearmans correlation analysis of noisemetrics against subjective responses andSudoku puzzle task performance The resultsare analyzed first with all signals includedand then in two groups separated by back-ground noise level (40 or 55 dBA) Boldedvalues indicate metrics chosen for use inthe regression model based on their overallhigh significant correlation values

All signals (40 dBA and 55 dBA BNL)

Loudness Annoyance TLX-avgPR 0150 0186 0147TNR 0123 0081 0095ΔLta 0006 0056 0019Aures 0297 0359 0314dB 0805 0824 0772dBA 0866 0887 0842ANSI loudness 0946 0950 0926ISO loudness 0938 0952 0925PNL 0892 0920 0886PNLT 0869 0877 0826JNM 0840 0869 0818SQI 0904 0899 0856

40 dBA BNL only

PR 0794 0867 0782TNR 0794 0867 0782ΔLta 0778 0888 0815Aures 0673 0709 0697dB 0806 0939 0855dBA 0794 0927 0830ANSI loudness 0685 0745 0697ISO loudness 0685 0745 0697PNL 0685 0842 0867PNLT 0794 0830 0758JNM 0794 0927 0830SQI 0806 0806 0709

55 dBA BNL only

PR 0799 0867 0758TNR 0709 0845 0845ΔLta 0787 0891 0818Aures 0781 0903 0782dB 0715 0756 0530dBA 0707 0770 0564ANSI loudness 0878 0855 0709ISO loudness 0817 0867 0697PNL 0720 0806 0539PNLT 0744 0782 0527JNM 0707 0770 0564SQI 0689 0663 0444

Correlation is significant at the 001 level (2-tailed)Correlation is significant at the 005 level (2-tailed)

79Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

for a linear multiple regression model for annoyancebecause these two metrics resulted in among the stron-gest correlations with annoyance perception compared

to other noise metrics Equation (2) presents the multi-variate regression model with ANSI loudness level andtonal audibility

Annoyance frac14 1806 thorn 1164 ANSI Loudness soneeth THORNfrac12 thorn 0072 Tonal Audibility dBeth THORNfrac12

eth2THORN

Table 5 also presents standard error of coefficientsstandardized coefficients and statistical significance whenANSI loudness level was only used (in step 1) and whentonal audibility was also included (in step 2) in additionto the coefficient values for each predictor Standardizedb values indicate the number of standard deviations thatthe outcome annoyance will change as a result of onestandard deviation change in the predictor The R2 valuefor the first step model is 0943 which is a measure ofgoodness-of-fit of linear regression indicating that 943of the annoyance rating variance can be explained by theANSI loudness model only When including tonal audibil-ity as a second predictor the R2 value increased to 0962Even though this increase is small the multivariate re-gression model does significantly predict more variationin annoyance perception when including tonal audibilityas a second predictor for step 2 the ANSI loudnesslevel [t(17) = 20796 p lt 0001] and tonal audibility[t(17) = 2943 p = 0009] are both significant predictorsof annoyance Figure 9 illustrates a regression line withthe calculated linear model

The results of the correlation analysis and regressionmodel presented in this paper are in line with the find-ings from More and Daviesrsquo study8 which focused onaircraft flyover noise rather than building mechanicalsystem noise Their work focused only on annoyanceand used metric values that were exceeded some per-centage (often 5) of the time since their flyover

Fig 8mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals against (a) ANSIloudness level for all signals and(b) Tonal audibility for 40 and 55 dBABNL separately Dashed lines indicateregression lines of annoyance ratingprediction with regard to each metric

Table 5mdashLinear regression model of predictors for annoyance perception with 95 bias corrected and accel-erated confidence intervals reported in parentheses Confidence intervals and standard errors arebased on 1000 bootstrap samples Standardized b values indicate the number of standard deviationsthat the outcome annoyance will change as a result of one standard deviation change in the predictor

b Standarderror B

b p

Step 1Constant 3254(2305 4310) 0512 p = 0001ANSI loudness (sone) 1137(1004 1263) 0066 0971 p = 0001

Step 2Constant 1806(0498 3187) 0683 p = 0020ANSI loudness (sone) 1164(1043 1308) 0069 0994 p = 0001Tonal audibility (dB) 0072(0027 0111) 0021 0141 p = 0004

Note 0943 for Step 1 ΔR2 = 0019 for Step 2

80 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

signals varied in time They demonstrated that the bestregression model when using existing metrics to matchtheir annoyance responses included both a loudnessmetric and a tonality metric and they proposed revi-sion to the penalty values used for the Joint NordicMethod to improve its individual predictive capabilityThe authors feel though that it is not necessary tomodify a combined metric (like Joint Nordic Method)since using individual metrics for loudness and for to-nality in the proposed regression model herein demon-strated high correlations to the annoyance responseson their own

4 SUMMARYAND CONCLUSION

The purpose of this study was to investigate hownoise signals with varying degrees of prominent tonessimilar to those produced by building mechanicalequipment affect subjective annoyance perception andtask performance and to develop a prediction modelof annoyance using current noise metrics Subjectscompleted Sudoku puzzles and a questionnaire modi-fied from the NASA task load index to quantify theoverall workload caused by building mechanical noisein this study No statistically significant effect was foundbetween the tonal signals used in this study and task per-formance although there was a trend of decreasing accu-racy with increasing tone strengths based on correctSudoku puzzle answers The validity of the modified taskload index questionnaire was high based on its reliabilitycoefficient and testndashretest coefficient and the average re-sponse from the questionnaire was found to significantlycorrelate with perceived annoyance and loudness of thebackground noise signals A factorial repeated measure

ANOVA revealed that participants felt more annoyed withincreasing background noise level lower tone frequencyand higher tone strength Correlation analysis with noisemetrics and subjective perception ratings found that ANSIloudness level among all other loudness metrics correlatesmost strongly with annoyance perception while assortedtonality metrics showed relatively weaker but still statisti-cally significant correlations with annoyance A statisticallysignificant multivariate regression model with ANSI loud-ness level and tonal audibility has been developed whichdemonstrates an R2 value of 0962

While noise sensitivity of test subjects was surveyedno statistically significant relations between perceptionor performance results and noise sensitivity were foundlikely due to the limited number of test subjects Futurework in this area is suggested with more test subjectsand more tonal signals to understand better the roleof noise sensitivity Also tonal noises from actual buildingmechanical systems often demonstrate multiple tones whichmay be inharmonic or which can fluctuate in time addi-tional investigations using tonal signals that incorporatethese other factors are recommended

5 ACKNOWLEDGMENTS

Many thanks to the undergraduate research assis-tants Adam Steinbach and Kristin Hanna who assistedwith the study design subjective testing and data analysisPartial funding for this project came from an Institute ofNoise Control Engineering Undergraduate Research Grant

6 REFERENCES

1 International Organization for Standardization (ISO) ldquoAcousticsmdashDescription Measurement and Assessment of EnvironmentalNoisemdashPart 2 Determination of Environmental Noise LevelsrdquoInternational Standard ISO 1996ndash22007 International Organi-zation for Standardization (2007)

2 ldquoSeattle Municipal Code Chapter 2508 Noise Controlrdquo Seattle(2007)

3 ldquoNoise Control Ordinance of the County of Los Angelesrdquo LosAngeles County (1978)

4 ldquoOrdinance of Minnesota Chapter 389 ndash Noiserdquo Minnesota(2008)

5 NY ADC LAW 27-770 NY code ndash Section 27-770 ldquoNoiseControl of Mechanical Equipmentrdquo New York (2006)

6 RP Hellman ldquoGrowth rate of loudness annoyance and noisi-ness as a function of tone location within the noise spectrumrdquoJ Acoust Soc Am 75(1) 209ndash218 (1984)

7 RP Hellman ldquoPerceived magnitude of two-tone-noise com-plexes loudness annoyance and noisinessrdquo J Acoust SocAm 77(4) 1497ndash1504 (1985)

8 S More and P Davies ldquoHuman responses to the tonalness of air-craft noiserdquo Noise Control Engr J 58(4) 420ndash440 (2010)

9 KH Lee P Davies and AM Surprenant ldquoQuantification ofthe tonal prominence of complex tones in machinery noiserdquoNoiseCon04 (2004)

Fig 9mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals plotted against theproposed linear regression model ofannoyance perception (dashed) basedon ANSI loudness level and tonalaudibility (R2 = 096)

81Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

10 A Hastings H Lee P Davies and AM Surprenant ldquoMeasure-ment of the attributes of complex tonal components commonlyfound in product soundrdquo Noise Control Engr J 51(4) 195ndash209(2003)

11 EE Ryherd and LM Wang ldquoImplications of human perfor-mance and perception under tonal noise conditions on indoornoise criteriardquo J Acoust Soc Am 124(1) 218ndash226 (2008)

12 P Susini S McAdams S Winsberg I Perry S Vieillard andX Rodet ldquoCharacterizing the sound quality of air-conditioningnoiserdquo Appl Acoust 65(8) 763ndash790 (2004)

13 B Berglund P Hassmeacuten and A Preis ldquoAnnoyance and spectralcontrast are cues for similarity and preference of soundsrdquo J SoundVibr 250(1) 53ndash64 (2002)

14 U Landstroumlm E Aringkerlund A Kjellberg and M Tesarz ldquoEx-posure levels tonal components and noise annoyance in work-ing environmentsrdquo Environ Int 21(3) 265ndash275 (1995)

15 U Landstroumlm A Kjellberg and M Bystroumlm ldquoAcceptablelevels of sounds with different spectral characteristics duringthe performance of a simple and a complex non-auditory taskrdquoJ Sound Vibr 160(3) 533ndash542 (1993)

16 D Laird ldquoThe influence of noise on production and fatigue asrelated to pitch sensation level and steadiness of the noiserdquo JAppl Psychol 17(3) 320ndash330 (1933)

17 JV Grjmaldi ldquoSensori-motor performance under varying noiseconditionsrdquo Ergonomics 2(1) 34ndash43 (1958)

18 K Holmberg U Landstrom and A Kjellberg ldquoEffects of ven-tilation noise due to frequency characteristic and sound levelrdquoJ Low Freq Noise Vib 12(4) 115ndash122 (1993)

19 International Organization for Standardization (ISO) ldquoAcousticsmdashAssessment of noise annoyance by means of social and socio-acoustic surveysrdquo International Standard ISOTS 15666 2003International Organization for Standardization (2003)

20 TH Pedersen ldquoThe ldquoGenlydrdquo noise annoyance model dose-response relationships modelled by logistic functionsrdquo DELTAHoslashrsholm Danmark (2007)

21 CMarquis-Favre E Premat andDAubreacutee ldquoNoise and its effectsmdasha review on qualitative aspects of sound Part II noise and annoy-ancerdquo Acta Acust united with Acust 91(4) 626ndash642 (2005)

22 American National Standards Institute (ANSI) ldquoAcousticsmdashMeasurement of Airborne Noise Emitted by Information Technol-ogy and Telecommunications EquipmentmdashPart 1 Determinationof Sound Power Level and Emission Sound Pressure LevelrdquoAmerican National Standards Institute ANSIASA S1210-2010Part 1 American Society of America (2010)

23 W Aures ldquoThe sensory euphony as a function of auditory sen-sationsrdquo Acustica 58(5) 282ndash290 (1985)

24 American National Standards Institute (ANSI) ldquoProcedure forthe Computation of Loudness of Steady Soundsrdquo AmericanNational Standards Institute ANSI S34-2007 Acoustical Soci-ety of America (2007)

25 ldquoAcousticsmdashMethod for Calculating Loudness Levelrdquo Interna-tional Standard ISO 5321975 International Organization forStandardization (1975)

26 E Zwicker ldquoSubdivision of the audible frequency range intocritical bands (Frequenzgruppen)rdquo J Acoust Soc Am 33(2)248ndash248 (1961)

27 BR Glasberg and BCJ Moore ldquoPrediction of absolutethresholds and equal loudness contours using a modified loud-ness modelrdquo J Acoust Soc Am 120 585ndash588 (2006)

28 Federal Aviation Administration ldquoPart 36 Noise StandardsAircraft Type and Airworthiness Certificationrdquo Federal Avia-tion Regulations Federal Aviation Administration (1969)

29 Air-Conditioning Heating and Refrigeration Institute (AHRI)ldquoSound Quality Evaluation Procedures for Air-Conditioningand Refrigeration Equipmentrdquo Air-Conditioning Heating andRefrigeration Institute AHRIANSI 1140-2012 Air-ConditioningHeating and Refrigeration Institute (2012)

30 WE Blazier ldquoRevised noise criteria for application in theacoustical design and rating of HVAC systemsrdquo Noise ControlEngr 16(2) 64ndash73 (1981)

31 F Faul E Erdfelder A Buchner and A-G Lang ldquoStatisticalpower analyses using GPower 31 tests for correlation and re-gression analysesrdquo Behav Res Methods 41 1149ndash1160 (2009)

32 M Schutte A Marks E Wenning and B Griefahn ldquoThe de-velopment of the noise sensitivity questionnairerdquo Noise Heal9(34) 15 (2007)

33 J Francis J Lee A Steinbach and LM Wang ldquoDeterminingannoyance thresholds of tones in noiserdquo ASHRAE Trans120 (2014)

34 JW Grabbe ldquoSudoku and working memory performance forolder adultsrdquo Act Adapt Aging 35(3) 241ndash254 (2011)

35 HS Chang and JM Gibson ldquoThe oddndasheven effect in Sudokupuzzles effects of working memory aging and experiencerdquoAm J Psychol 124(3) 313ndash324 (2011)

36 ldquoNASA TLX Task Load Indexrdquo [Online] Available at httphumansystemsarcnasagovgroupstlx (accessed September 92015)

37 RFS Job J Hatfield NL Carter P Peploe R Taylor andS Morrell ldquoGeneral scales of community reaction to noise(dissatisfaction and perceived affectedness) are more reliablethan scales of annoyancerdquo J Acoust Soc Am 110(2) 939ndash946(2001)

82 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

first with all twenty tonal signals included and then withthe average ratings for ten signals grouped separately bythe broadband background noise level (40 or 55 dBA)Table 4 presents correlation coefficients between all noisemetrics with the subjective perception responses

When analyzing all signals the noise metric thatdemonstrates the highest correlation coefficients withthe perceived loudness annoyance and TLX-avg rat-ings is ANSI loudness level Other loudness metricswere also significantly correlated to the perception rat-ings but the tonality metrics such as prominence ratiotone-to-noise ratio tonality audibility and Aures tonal-ity did not statistically correlate or had lower coefficientsthan loudness metrics This confirms that loudness is themost dominant factor in determining subjective percep-tion of noise

When the signals are grouped separately by broadbandbackground noise levels though tonality metrics didshow higher correlations with subjective ratings thanloudness metrics The coefficient values for the assortedtonality metrics are all very similar with no particularmetric clearly performing better than others Howeverwhen only looking for correlation coefficients with an-noyance tonal audibility showed slightly higher corre-lation coefficients than other tonality metrics (0888for 40 dBA BNL and 0891 for 55 dBA BNL) Aurestonality also showed high correlation with annoyancefrom 55 dBA BNL signals (0903) but it showed lowercorrelation than other metrics with 40 dBA BNL sig-nals (0709) The results indicate that when the broad-band background noise level is controlled or comparabletonality becomes a more influencing factor on annoyanceevaluation Figure 8 presents scatterplots of the averagedannoyance responses (a) with the ANSI loudness levelacross the entire group and (b) with tonal audibility sepa-rated by background noise level

For all cases combined metrics such as the JointNordic Method tone-corrected perceived noise leveland sound quality indicator did not show remarkablybetter performance than loudness metrics even thoughthese combined metrics were significantly related withannoyance ratings The results suggest that imposingpenalty values to loudness levels based on tonal strengthmay not be the most appropriate way to quantify overallsubjective annoyance of tonal noise Instead using sepa-rate metrics to account for tonality and loudness of build-ing mechanical noises is an effective way to relate to thesignals annoyance

34 Regression Model between Noise Metricsand Annoyance

Based on the results in Table 4 ANSI loudness leveland tonal audibility were selected to be used as predictors

Table 4mdashSpearmans correlation analysis of noisemetrics against subjective responses andSudoku puzzle task performance The resultsare analyzed first with all signals includedand then in two groups separated by back-ground noise level (40 or 55 dBA) Boldedvalues indicate metrics chosen for use inthe regression model based on their overallhigh significant correlation values

All signals (40 dBA and 55 dBA BNL)

Loudness Annoyance TLX-avgPR 0150 0186 0147TNR 0123 0081 0095ΔLta 0006 0056 0019Aures 0297 0359 0314dB 0805 0824 0772dBA 0866 0887 0842ANSI loudness 0946 0950 0926ISO loudness 0938 0952 0925PNL 0892 0920 0886PNLT 0869 0877 0826JNM 0840 0869 0818SQI 0904 0899 0856

40 dBA BNL only

PR 0794 0867 0782TNR 0794 0867 0782ΔLta 0778 0888 0815Aures 0673 0709 0697dB 0806 0939 0855dBA 0794 0927 0830ANSI loudness 0685 0745 0697ISO loudness 0685 0745 0697PNL 0685 0842 0867PNLT 0794 0830 0758JNM 0794 0927 0830SQI 0806 0806 0709

55 dBA BNL only

PR 0799 0867 0758TNR 0709 0845 0845ΔLta 0787 0891 0818Aures 0781 0903 0782dB 0715 0756 0530dBA 0707 0770 0564ANSI loudness 0878 0855 0709ISO loudness 0817 0867 0697PNL 0720 0806 0539PNLT 0744 0782 0527JNM 0707 0770 0564SQI 0689 0663 0444

Correlation is significant at the 001 level (2-tailed)Correlation is significant at the 005 level (2-tailed)

79Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

for a linear multiple regression model for annoyancebecause these two metrics resulted in among the stron-gest correlations with annoyance perception compared

to other noise metrics Equation (2) presents the multi-variate regression model with ANSI loudness level andtonal audibility

Annoyance frac14 1806 thorn 1164 ANSI Loudness soneeth THORNfrac12 thorn 0072 Tonal Audibility dBeth THORNfrac12

eth2THORN

Table 5 also presents standard error of coefficientsstandardized coefficients and statistical significance whenANSI loudness level was only used (in step 1) and whentonal audibility was also included (in step 2) in additionto the coefficient values for each predictor Standardizedb values indicate the number of standard deviations thatthe outcome annoyance will change as a result of onestandard deviation change in the predictor The R2 valuefor the first step model is 0943 which is a measure ofgoodness-of-fit of linear regression indicating that 943of the annoyance rating variance can be explained by theANSI loudness model only When including tonal audibil-ity as a second predictor the R2 value increased to 0962Even though this increase is small the multivariate re-gression model does significantly predict more variationin annoyance perception when including tonal audibilityas a second predictor for step 2 the ANSI loudnesslevel [t(17) = 20796 p lt 0001] and tonal audibility[t(17) = 2943 p = 0009] are both significant predictorsof annoyance Figure 9 illustrates a regression line withthe calculated linear model

The results of the correlation analysis and regressionmodel presented in this paper are in line with the find-ings from More and Daviesrsquo study8 which focused onaircraft flyover noise rather than building mechanicalsystem noise Their work focused only on annoyanceand used metric values that were exceeded some per-centage (often 5) of the time since their flyover

Fig 8mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals against (a) ANSIloudness level for all signals and(b) Tonal audibility for 40 and 55 dBABNL separately Dashed lines indicateregression lines of annoyance ratingprediction with regard to each metric

Table 5mdashLinear regression model of predictors for annoyance perception with 95 bias corrected and accel-erated confidence intervals reported in parentheses Confidence intervals and standard errors arebased on 1000 bootstrap samples Standardized b values indicate the number of standard deviationsthat the outcome annoyance will change as a result of one standard deviation change in the predictor

b Standarderror B

b p

Step 1Constant 3254(2305 4310) 0512 p = 0001ANSI loudness (sone) 1137(1004 1263) 0066 0971 p = 0001

Step 2Constant 1806(0498 3187) 0683 p = 0020ANSI loudness (sone) 1164(1043 1308) 0069 0994 p = 0001Tonal audibility (dB) 0072(0027 0111) 0021 0141 p = 0004

Note 0943 for Step 1 ΔR2 = 0019 for Step 2

80 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

signals varied in time They demonstrated that the bestregression model when using existing metrics to matchtheir annoyance responses included both a loudnessmetric and a tonality metric and they proposed revi-sion to the penalty values used for the Joint NordicMethod to improve its individual predictive capabilityThe authors feel though that it is not necessary tomodify a combined metric (like Joint Nordic Method)since using individual metrics for loudness and for to-nality in the proposed regression model herein demon-strated high correlations to the annoyance responseson their own

4 SUMMARYAND CONCLUSION

The purpose of this study was to investigate hownoise signals with varying degrees of prominent tonessimilar to those produced by building mechanicalequipment affect subjective annoyance perception andtask performance and to develop a prediction modelof annoyance using current noise metrics Subjectscompleted Sudoku puzzles and a questionnaire modi-fied from the NASA task load index to quantify theoverall workload caused by building mechanical noisein this study No statistically significant effect was foundbetween the tonal signals used in this study and task per-formance although there was a trend of decreasing accu-racy with increasing tone strengths based on correctSudoku puzzle answers The validity of the modified taskload index questionnaire was high based on its reliabilitycoefficient and testndashretest coefficient and the average re-sponse from the questionnaire was found to significantlycorrelate with perceived annoyance and loudness of thebackground noise signals A factorial repeated measure

ANOVA revealed that participants felt more annoyed withincreasing background noise level lower tone frequencyand higher tone strength Correlation analysis with noisemetrics and subjective perception ratings found that ANSIloudness level among all other loudness metrics correlatesmost strongly with annoyance perception while assortedtonality metrics showed relatively weaker but still statisti-cally significant correlations with annoyance A statisticallysignificant multivariate regression model with ANSI loud-ness level and tonal audibility has been developed whichdemonstrates an R2 value of 0962

While noise sensitivity of test subjects was surveyedno statistically significant relations between perceptionor performance results and noise sensitivity were foundlikely due to the limited number of test subjects Futurework in this area is suggested with more test subjectsand more tonal signals to understand better the roleof noise sensitivity Also tonal noises from actual buildingmechanical systems often demonstrate multiple tones whichmay be inharmonic or which can fluctuate in time addi-tional investigations using tonal signals that incorporatethese other factors are recommended

5 ACKNOWLEDGMENTS

Many thanks to the undergraduate research assis-tants Adam Steinbach and Kristin Hanna who assistedwith the study design subjective testing and data analysisPartial funding for this project came from an Institute ofNoise Control Engineering Undergraduate Research Grant

6 REFERENCES

1 International Organization for Standardization (ISO) ldquoAcousticsmdashDescription Measurement and Assessment of EnvironmentalNoisemdashPart 2 Determination of Environmental Noise LevelsrdquoInternational Standard ISO 1996ndash22007 International Organi-zation for Standardization (2007)

2 ldquoSeattle Municipal Code Chapter 2508 Noise Controlrdquo Seattle(2007)

3 ldquoNoise Control Ordinance of the County of Los Angelesrdquo LosAngeles County (1978)

4 ldquoOrdinance of Minnesota Chapter 389 ndash Noiserdquo Minnesota(2008)

5 NY ADC LAW 27-770 NY code ndash Section 27-770 ldquoNoiseControl of Mechanical Equipmentrdquo New York (2006)

6 RP Hellman ldquoGrowth rate of loudness annoyance and noisi-ness as a function of tone location within the noise spectrumrdquoJ Acoust Soc Am 75(1) 209ndash218 (1984)

7 RP Hellman ldquoPerceived magnitude of two-tone-noise com-plexes loudness annoyance and noisinessrdquo J Acoust SocAm 77(4) 1497ndash1504 (1985)

8 S More and P Davies ldquoHuman responses to the tonalness of air-craft noiserdquo Noise Control Engr J 58(4) 420ndash440 (2010)

9 KH Lee P Davies and AM Surprenant ldquoQuantification ofthe tonal prominence of complex tones in machinery noiserdquoNoiseCon04 (2004)

Fig 9mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals plotted against theproposed linear regression model ofannoyance perception (dashed) basedon ANSI loudness level and tonalaudibility (R2 = 096)

81Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

10 A Hastings H Lee P Davies and AM Surprenant ldquoMeasure-ment of the attributes of complex tonal components commonlyfound in product soundrdquo Noise Control Engr J 51(4) 195ndash209(2003)

11 EE Ryherd and LM Wang ldquoImplications of human perfor-mance and perception under tonal noise conditions on indoornoise criteriardquo J Acoust Soc Am 124(1) 218ndash226 (2008)

12 P Susini S McAdams S Winsberg I Perry S Vieillard andX Rodet ldquoCharacterizing the sound quality of air-conditioningnoiserdquo Appl Acoust 65(8) 763ndash790 (2004)

13 B Berglund P Hassmeacuten and A Preis ldquoAnnoyance and spectralcontrast are cues for similarity and preference of soundsrdquo J SoundVibr 250(1) 53ndash64 (2002)

14 U Landstroumlm E Aringkerlund A Kjellberg and M Tesarz ldquoEx-posure levels tonal components and noise annoyance in work-ing environmentsrdquo Environ Int 21(3) 265ndash275 (1995)

15 U Landstroumlm A Kjellberg and M Bystroumlm ldquoAcceptablelevels of sounds with different spectral characteristics duringthe performance of a simple and a complex non-auditory taskrdquoJ Sound Vibr 160(3) 533ndash542 (1993)

16 D Laird ldquoThe influence of noise on production and fatigue asrelated to pitch sensation level and steadiness of the noiserdquo JAppl Psychol 17(3) 320ndash330 (1933)

17 JV Grjmaldi ldquoSensori-motor performance under varying noiseconditionsrdquo Ergonomics 2(1) 34ndash43 (1958)

18 K Holmberg U Landstrom and A Kjellberg ldquoEffects of ven-tilation noise due to frequency characteristic and sound levelrdquoJ Low Freq Noise Vib 12(4) 115ndash122 (1993)

19 International Organization for Standardization (ISO) ldquoAcousticsmdashAssessment of noise annoyance by means of social and socio-acoustic surveysrdquo International Standard ISOTS 15666 2003International Organization for Standardization (2003)

20 TH Pedersen ldquoThe ldquoGenlydrdquo noise annoyance model dose-response relationships modelled by logistic functionsrdquo DELTAHoslashrsholm Danmark (2007)

21 CMarquis-Favre E Premat andDAubreacutee ldquoNoise and its effectsmdasha review on qualitative aspects of sound Part II noise and annoy-ancerdquo Acta Acust united with Acust 91(4) 626ndash642 (2005)

22 American National Standards Institute (ANSI) ldquoAcousticsmdashMeasurement of Airborne Noise Emitted by Information Technol-ogy and Telecommunications EquipmentmdashPart 1 Determinationof Sound Power Level and Emission Sound Pressure LevelrdquoAmerican National Standards Institute ANSIASA S1210-2010Part 1 American Society of America (2010)

23 W Aures ldquoThe sensory euphony as a function of auditory sen-sationsrdquo Acustica 58(5) 282ndash290 (1985)

24 American National Standards Institute (ANSI) ldquoProcedure forthe Computation of Loudness of Steady Soundsrdquo AmericanNational Standards Institute ANSI S34-2007 Acoustical Soci-ety of America (2007)

25 ldquoAcousticsmdashMethod for Calculating Loudness Levelrdquo Interna-tional Standard ISO 5321975 International Organization forStandardization (1975)

26 E Zwicker ldquoSubdivision of the audible frequency range intocritical bands (Frequenzgruppen)rdquo J Acoust Soc Am 33(2)248ndash248 (1961)

27 BR Glasberg and BCJ Moore ldquoPrediction of absolutethresholds and equal loudness contours using a modified loud-ness modelrdquo J Acoust Soc Am 120 585ndash588 (2006)

28 Federal Aviation Administration ldquoPart 36 Noise StandardsAircraft Type and Airworthiness Certificationrdquo Federal Avia-tion Regulations Federal Aviation Administration (1969)

29 Air-Conditioning Heating and Refrigeration Institute (AHRI)ldquoSound Quality Evaluation Procedures for Air-Conditioningand Refrigeration Equipmentrdquo Air-Conditioning Heating andRefrigeration Institute AHRIANSI 1140-2012 Air-ConditioningHeating and Refrigeration Institute (2012)

30 WE Blazier ldquoRevised noise criteria for application in theacoustical design and rating of HVAC systemsrdquo Noise ControlEngr 16(2) 64ndash73 (1981)

31 F Faul E Erdfelder A Buchner and A-G Lang ldquoStatisticalpower analyses using GPower 31 tests for correlation and re-gression analysesrdquo Behav Res Methods 41 1149ndash1160 (2009)

32 M Schutte A Marks E Wenning and B Griefahn ldquoThe de-velopment of the noise sensitivity questionnairerdquo Noise Heal9(34) 15 (2007)

33 J Francis J Lee A Steinbach and LM Wang ldquoDeterminingannoyance thresholds of tones in noiserdquo ASHRAE Trans120 (2014)

34 JW Grabbe ldquoSudoku and working memory performance forolder adultsrdquo Act Adapt Aging 35(3) 241ndash254 (2011)

35 HS Chang and JM Gibson ldquoThe oddndasheven effect in Sudokupuzzles effects of working memory aging and experiencerdquoAm J Psychol 124(3) 313ndash324 (2011)

36 ldquoNASA TLX Task Load Indexrdquo [Online] Available at httphumansystemsarcnasagovgroupstlx (accessed September 92015)

37 RFS Job J Hatfield NL Carter P Peploe R Taylor andS Morrell ldquoGeneral scales of community reaction to noise(dissatisfaction and perceived affectedness) are more reliablethan scales of annoyancerdquo J Acoust Soc Am 110(2) 939ndash946(2001)

82 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

for a linear multiple regression model for annoyancebecause these two metrics resulted in among the stron-gest correlations with annoyance perception compared

to other noise metrics Equation (2) presents the multi-variate regression model with ANSI loudness level andtonal audibility

Annoyance frac14 1806 thorn 1164 ANSI Loudness soneeth THORNfrac12 thorn 0072 Tonal Audibility dBeth THORNfrac12

eth2THORN

Table 5 also presents standard error of coefficientsstandardized coefficients and statistical significance whenANSI loudness level was only used (in step 1) and whentonal audibility was also included (in step 2) in additionto the coefficient values for each predictor Standardizedb values indicate the number of standard deviations thatthe outcome annoyance will change as a result of onestandard deviation change in the predictor The R2 valuefor the first step model is 0943 which is a measure ofgoodness-of-fit of linear regression indicating that 943of the annoyance rating variance can be explained by theANSI loudness model only When including tonal audibil-ity as a second predictor the R2 value increased to 0962Even though this increase is small the multivariate re-gression model does significantly predict more variationin annoyance perception when including tonal audibilityas a second predictor for step 2 the ANSI loudnesslevel [t(17) = 20796 p lt 0001] and tonal audibility[t(17) = 2943 p = 0009] are both significant predictorsof annoyance Figure 9 illustrates a regression line withthe calculated linear model

The results of the correlation analysis and regressionmodel presented in this paper are in line with the find-ings from More and Daviesrsquo study8 which focused onaircraft flyover noise rather than building mechanicalsystem noise Their work focused only on annoyanceand used metric values that were exceeded some per-centage (often 5) of the time since their flyover

Fig 8mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals against (a) ANSIloudness level for all signals and(b) Tonal audibility for 40 and 55 dBABNL separately Dashed lines indicateregression lines of annoyance ratingprediction with regard to each metric

Table 5mdashLinear regression model of predictors for annoyance perception with 95 bias corrected and accel-erated confidence intervals reported in parentheses Confidence intervals and standard errors arebased on 1000 bootstrap samples Standardized b values indicate the number of standard deviationsthat the outcome annoyance will change as a result of one standard deviation change in the predictor

b Standarderror B

b p

Step 1Constant 3254(2305 4310) 0512 p = 0001ANSI loudness (sone) 1137(1004 1263) 0066 0971 p = 0001

Step 2Constant 1806(0498 3187) 0683 p = 0020ANSI loudness (sone) 1164(1043 1308) 0069 0994 p = 0001Tonal audibility (dB) 0072(0027 0111) 0021 0141 p = 0004

Note 0943 for Step 1 ΔR2 = 0019 for Step 2

80 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

signals varied in time They demonstrated that the bestregression model when using existing metrics to matchtheir annoyance responses included both a loudnessmetric and a tonality metric and they proposed revi-sion to the penalty values used for the Joint NordicMethod to improve its individual predictive capabilityThe authors feel though that it is not necessary tomodify a combined metric (like Joint Nordic Method)since using individual metrics for loudness and for to-nality in the proposed regression model herein demon-strated high correlations to the annoyance responseson their own

4 SUMMARYAND CONCLUSION

The purpose of this study was to investigate hownoise signals with varying degrees of prominent tonessimilar to those produced by building mechanicalequipment affect subjective annoyance perception andtask performance and to develop a prediction modelof annoyance using current noise metrics Subjectscompleted Sudoku puzzles and a questionnaire modi-fied from the NASA task load index to quantify theoverall workload caused by building mechanical noisein this study No statistically significant effect was foundbetween the tonal signals used in this study and task per-formance although there was a trend of decreasing accu-racy with increasing tone strengths based on correctSudoku puzzle answers The validity of the modified taskload index questionnaire was high based on its reliabilitycoefficient and testndashretest coefficient and the average re-sponse from the questionnaire was found to significantlycorrelate with perceived annoyance and loudness of thebackground noise signals A factorial repeated measure

ANOVA revealed that participants felt more annoyed withincreasing background noise level lower tone frequencyand higher tone strength Correlation analysis with noisemetrics and subjective perception ratings found that ANSIloudness level among all other loudness metrics correlatesmost strongly with annoyance perception while assortedtonality metrics showed relatively weaker but still statisti-cally significant correlations with annoyance A statisticallysignificant multivariate regression model with ANSI loud-ness level and tonal audibility has been developed whichdemonstrates an R2 value of 0962

While noise sensitivity of test subjects was surveyedno statistically significant relations between perceptionor performance results and noise sensitivity were foundlikely due to the limited number of test subjects Futurework in this area is suggested with more test subjectsand more tonal signals to understand better the roleof noise sensitivity Also tonal noises from actual buildingmechanical systems often demonstrate multiple tones whichmay be inharmonic or which can fluctuate in time addi-tional investigations using tonal signals that incorporatethese other factors are recommended

5 ACKNOWLEDGMENTS

Many thanks to the undergraduate research assis-tants Adam Steinbach and Kristin Hanna who assistedwith the study design subjective testing and data analysisPartial funding for this project came from an Institute ofNoise Control Engineering Undergraduate Research Grant

6 REFERENCES

1 International Organization for Standardization (ISO) ldquoAcousticsmdashDescription Measurement and Assessment of EnvironmentalNoisemdashPart 2 Determination of Environmental Noise LevelsrdquoInternational Standard ISO 1996ndash22007 International Organi-zation for Standardization (2007)

2 ldquoSeattle Municipal Code Chapter 2508 Noise Controlrdquo Seattle(2007)

3 ldquoNoise Control Ordinance of the County of Los Angelesrdquo LosAngeles County (1978)

4 ldquoOrdinance of Minnesota Chapter 389 ndash Noiserdquo Minnesota(2008)

5 NY ADC LAW 27-770 NY code ndash Section 27-770 ldquoNoiseControl of Mechanical Equipmentrdquo New York (2006)

6 RP Hellman ldquoGrowth rate of loudness annoyance and noisi-ness as a function of tone location within the noise spectrumrdquoJ Acoust Soc Am 75(1) 209ndash218 (1984)

7 RP Hellman ldquoPerceived magnitude of two-tone-noise com-plexes loudness annoyance and noisinessrdquo J Acoust SocAm 77(4) 1497ndash1504 (1985)

8 S More and P Davies ldquoHuman responses to the tonalness of air-craft noiserdquo Noise Control Engr J 58(4) 420ndash440 (2010)

9 KH Lee P Davies and AM Surprenant ldquoQuantification ofthe tonal prominence of complex tones in machinery noiserdquoNoiseCon04 (2004)

Fig 9mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals plotted against theproposed linear regression model ofannoyance perception (dashed) basedon ANSI loudness level and tonalaudibility (R2 = 096)

81Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

10 A Hastings H Lee P Davies and AM Surprenant ldquoMeasure-ment of the attributes of complex tonal components commonlyfound in product soundrdquo Noise Control Engr J 51(4) 195ndash209(2003)

11 EE Ryherd and LM Wang ldquoImplications of human perfor-mance and perception under tonal noise conditions on indoornoise criteriardquo J Acoust Soc Am 124(1) 218ndash226 (2008)

12 P Susini S McAdams S Winsberg I Perry S Vieillard andX Rodet ldquoCharacterizing the sound quality of air-conditioningnoiserdquo Appl Acoust 65(8) 763ndash790 (2004)

13 B Berglund P Hassmeacuten and A Preis ldquoAnnoyance and spectralcontrast are cues for similarity and preference of soundsrdquo J SoundVibr 250(1) 53ndash64 (2002)

14 U Landstroumlm E Aringkerlund A Kjellberg and M Tesarz ldquoEx-posure levels tonal components and noise annoyance in work-ing environmentsrdquo Environ Int 21(3) 265ndash275 (1995)

15 U Landstroumlm A Kjellberg and M Bystroumlm ldquoAcceptablelevels of sounds with different spectral characteristics duringthe performance of a simple and a complex non-auditory taskrdquoJ Sound Vibr 160(3) 533ndash542 (1993)

16 D Laird ldquoThe influence of noise on production and fatigue asrelated to pitch sensation level and steadiness of the noiserdquo JAppl Psychol 17(3) 320ndash330 (1933)

17 JV Grjmaldi ldquoSensori-motor performance under varying noiseconditionsrdquo Ergonomics 2(1) 34ndash43 (1958)

18 K Holmberg U Landstrom and A Kjellberg ldquoEffects of ven-tilation noise due to frequency characteristic and sound levelrdquoJ Low Freq Noise Vib 12(4) 115ndash122 (1993)

19 International Organization for Standardization (ISO) ldquoAcousticsmdashAssessment of noise annoyance by means of social and socio-acoustic surveysrdquo International Standard ISOTS 15666 2003International Organization for Standardization (2003)

20 TH Pedersen ldquoThe ldquoGenlydrdquo noise annoyance model dose-response relationships modelled by logistic functionsrdquo DELTAHoslashrsholm Danmark (2007)

21 CMarquis-Favre E Premat andDAubreacutee ldquoNoise and its effectsmdasha review on qualitative aspects of sound Part II noise and annoy-ancerdquo Acta Acust united with Acust 91(4) 626ndash642 (2005)

22 American National Standards Institute (ANSI) ldquoAcousticsmdashMeasurement of Airborne Noise Emitted by Information Technol-ogy and Telecommunications EquipmentmdashPart 1 Determinationof Sound Power Level and Emission Sound Pressure LevelrdquoAmerican National Standards Institute ANSIASA S1210-2010Part 1 American Society of America (2010)

23 W Aures ldquoThe sensory euphony as a function of auditory sen-sationsrdquo Acustica 58(5) 282ndash290 (1985)

24 American National Standards Institute (ANSI) ldquoProcedure forthe Computation of Loudness of Steady Soundsrdquo AmericanNational Standards Institute ANSI S34-2007 Acoustical Soci-ety of America (2007)

25 ldquoAcousticsmdashMethod for Calculating Loudness Levelrdquo Interna-tional Standard ISO 5321975 International Organization forStandardization (1975)

26 E Zwicker ldquoSubdivision of the audible frequency range intocritical bands (Frequenzgruppen)rdquo J Acoust Soc Am 33(2)248ndash248 (1961)

27 BR Glasberg and BCJ Moore ldquoPrediction of absolutethresholds and equal loudness contours using a modified loud-ness modelrdquo J Acoust Soc Am 120 585ndash588 (2006)

28 Federal Aviation Administration ldquoPart 36 Noise StandardsAircraft Type and Airworthiness Certificationrdquo Federal Avia-tion Regulations Federal Aviation Administration (1969)

29 Air-Conditioning Heating and Refrigeration Institute (AHRI)ldquoSound Quality Evaluation Procedures for Air-Conditioningand Refrigeration Equipmentrdquo Air-Conditioning Heating andRefrigeration Institute AHRIANSI 1140-2012 Air-ConditioningHeating and Refrigeration Institute (2012)

30 WE Blazier ldquoRevised noise criteria for application in theacoustical design and rating of HVAC systemsrdquo Noise ControlEngr 16(2) 64ndash73 (1981)

31 F Faul E Erdfelder A Buchner and A-G Lang ldquoStatisticalpower analyses using GPower 31 tests for correlation and re-gression analysesrdquo Behav Res Methods 41 1149ndash1160 (2009)

32 M Schutte A Marks E Wenning and B Griefahn ldquoThe de-velopment of the noise sensitivity questionnairerdquo Noise Heal9(34) 15 (2007)

33 J Francis J Lee A Steinbach and LM Wang ldquoDeterminingannoyance thresholds of tones in noiserdquo ASHRAE Trans120 (2014)

34 JW Grabbe ldquoSudoku and working memory performance forolder adultsrdquo Act Adapt Aging 35(3) 241ndash254 (2011)

35 HS Chang and JM Gibson ldquoThe oddndasheven effect in Sudokupuzzles effects of working memory aging and experiencerdquoAm J Psychol 124(3) 313ndash324 (2011)

36 ldquoNASA TLX Task Load Indexrdquo [Online] Available at httphumansystemsarcnasagovgroupstlx (accessed September 92015)

37 RFS Job J Hatfield NL Carter P Peploe R Taylor andS Morrell ldquoGeneral scales of community reaction to noise(dissatisfaction and perceived affectedness) are more reliablethan scales of annoyancerdquo J Acoust Soc Am 110(2) 939ndash946(2001)

82 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

signals varied in time They demonstrated that the bestregression model when using existing metrics to matchtheir annoyance responses included both a loudnessmetric and a tonality metric and they proposed revi-sion to the penalty values used for the Joint NordicMethod to improve its individual predictive capabilityThe authors feel though that it is not necessary tomodify a combined metric (like Joint Nordic Method)since using individual metrics for loudness and for to-nality in the proposed regression model herein demon-strated high correlations to the annoyance responseson their own

4 SUMMARYAND CONCLUSION

The purpose of this study was to investigate hownoise signals with varying degrees of prominent tonessimilar to those produced by building mechanicalequipment affect subjective annoyance perception andtask performance and to develop a prediction modelof annoyance using current noise metrics Subjectscompleted Sudoku puzzles and a questionnaire modi-fied from the NASA task load index to quantify theoverall workload caused by building mechanical noisein this study No statistically significant effect was foundbetween the tonal signals used in this study and task per-formance although there was a trend of decreasing accu-racy with increasing tone strengths based on correctSudoku puzzle answers The validity of the modified taskload index questionnaire was high based on its reliabilitycoefficient and testndashretest coefficient and the average re-sponse from the questionnaire was found to significantlycorrelate with perceived annoyance and loudness of thebackground noise signals A factorial repeated measure

ANOVA revealed that participants felt more annoyed withincreasing background noise level lower tone frequencyand higher tone strength Correlation analysis with noisemetrics and subjective perception ratings found that ANSIloudness level among all other loudness metrics correlatesmost strongly with annoyance perception while assortedtonality metrics showed relatively weaker but still statisti-cally significant correlations with annoyance A statisticallysignificant multivariate regression model with ANSI loud-ness level and tonal audibility has been developed whichdemonstrates an R2 value of 0962

While noise sensitivity of test subjects was surveyedno statistically significant relations between perceptionor performance results and noise sensitivity were foundlikely due to the limited number of test subjects Futurework in this area is suggested with more test subjectsand more tonal signals to understand better the roleof noise sensitivity Also tonal noises from actual buildingmechanical systems often demonstrate multiple tones whichmay be inharmonic or which can fluctuate in time addi-tional investigations using tonal signals that incorporatethese other factors are recommended

5 ACKNOWLEDGMENTS

Many thanks to the undergraduate research assis-tants Adam Steinbach and Kristin Hanna who assistedwith the study design subjective testing and data analysisPartial funding for this project came from an Institute ofNoise Control Engineering Undergraduate Research Grant

6 REFERENCES

1 International Organization for Standardization (ISO) ldquoAcousticsmdashDescription Measurement and Assessment of EnvironmentalNoisemdashPart 2 Determination of Environmental Noise LevelsrdquoInternational Standard ISO 1996ndash22007 International Organi-zation for Standardization (2007)

2 ldquoSeattle Municipal Code Chapter 2508 Noise Controlrdquo Seattle(2007)

3 ldquoNoise Control Ordinance of the County of Los Angelesrdquo LosAngeles County (1978)

4 ldquoOrdinance of Minnesota Chapter 389 ndash Noiserdquo Minnesota(2008)

5 NY ADC LAW 27-770 NY code ndash Section 27-770 ldquoNoiseControl of Mechanical Equipmentrdquo New York (2006)

6 RP Hellman ldquoGrowth rate of loudness annoyance and noisi-ness as a function of tone location within the noise spectrumrdquoJ Acoust Soc Am 75(1) 209ndash218 (1984)

7 RP Hellman ldquoPerceived magnitude of two-tone-noise com-plexes loudness annoyance and noisinessrdquo J Acoust SocAm 77(4) 1497ndash1504 (1985)

8 S More and P Davies ldquoHuman responses to the tonalness of air-craft noiserdquo Noise Control Engr J 58(4) 420ndash440 (2010)

9 KH Lee P Davies and AM Surprenant ldquoQuantification ofthe tonal prominence of complex tones in machinery noiserdquoNoiseCon04 (2004)

Fig 9mdashAverage (mark) and standard deviation(error bar) of the annoyance ratingsfor noise signals plotted against theproposed linear regression model ofannoyance perception (dashed) basedon ANSI loudness level and tonalaudibility (R2 = 096)

81Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

10 A Hastings H Lee P Davies and AM Surprenant ldquoMeasure-ment of the attributes of complex tonal components commonlyfound in product soundrdquo Noise Control Engr J 51(4) 195ndash209(2003)

11 EE Ryherd and LM Wang ldquoImplications of human perfor-mance and perception under tonal noise conditions on indoornoise criteriardquo J Acoust Soc Am 124(1) 218ndash226 (2008)

12 P Susini S McAdams S Winsberg I Perry S Vieillard andX Rodet ldquoCharacterizing the sound quality of air-conditioningnoiserdquo Appl Acoust 65(8) 763ndash790 (2004)

13 B Berglund P Hassmeacuten and A Preis ldquoAnnoyance and spectralcontrast are cues for similarity and preference of soundsrdquo J SoundVibr 250(1) 53ndash64 (2002)

14 U Landstroumlm E Aringkerlund A Kjellberg and M Tesarz ldquoEx-posure levels tonal components and noise annoyance in work-ing environmentsrdquo Environ Int 21(3) 265ndash275 (1995)

15 U Landstroumlm A Kjellberg and M Bystroumlm ldquoAcceptablelevels of sounds with different spectral characteristics duringthe performance of a simple and a complex non-auditory taskrdquoJ Sound Vibr 160(3) 533ndash542 (1993)

16 D Laird ldquoThe influence of noise on production and fatigue asrelated to pitch sensation level and steadiness of the noiserdquo JAppl Psychol 17(3) 320ndash330 (1933)

17 JV Grjmaldi ldquoSensori-motor performance under varying noiseconditionsrdquo Ergonomics 2(1) 34ndash43 (1958)

18 K Holmberg U Landstrom and A Kjellberg ldquoEffects of ven-tilation noise due to frequency characteristic and sound levelrdquoJ Low Freq Noise Vib 12(4) 115ndash122 (1993)

19 International Organization for Standardization (ISO) ldquoAcousticsmdashAssessment of noise annoyance by means of social and socio-acoustic surveysrdquo International Standard ISOTS 15666 2003International Organization for Standardization (2003)

20 TH Pedersen ldquoThe ldquoGenlydrdquo noise annoyance model dose-response relationships modelled by logistic functionsrdquo DELTAHoslashrsholm Danmark (2007)

21 CMarquis-Favre E Premat andDAubreacutee ldquoNoise and its effectsmdasha review on qualitative aspects of sound Part II noise and annoy-ancerdquo Acta Acust united with Acust 91(4) 626ndash642 (2005)

22 American National Standards Institute (ANSI) ldquoAcousticsmdashMeasurement of Airborne Noise Emitted by Information Technol-ogy and Telecommunications EquipmentmdashPart 1 Determinationof Sound Power Level and Emission Sound Pressure LevelrdquoAmerican National Standards Institute ANSIASA S1210-2010Part 1 American Society of America (2010)

23 W Aures ldquoThe sensory euphony as a function of auditory sen-sationsrdquo Acustica 58(5) 282ndash290 (1985)

24 American National Standards Institute (ANSI) ldquoProcedure forthe Computation of Loudness of Steady Soundsrdquo AmericanNational Standards Institute ANSI S34-2007 Acoustical Soci-ety of America (2007)

25 ldquoAcousticsmdashMethod for Calculating Loudness Levelrdquo Interna-tional Standard ISO 5321975 International Organization forStandardization (1975)

26 E Zwicker ldquoSubdivision of the audible frequency range intocritical bands (Frequenzgruppen)rdquo J Acoust Soc Am 33(2)248ndash248 (1961)

27 BR Glasberg and BCJ Moore ldquoPrediction of absolutethresholds and equal loudness contours using a modified loud-ness modelrdquo J Acoust Soc Am 120 585ndash588 (2006)

28 Federal Aviation Administration ldquoPart 36 Noise StandardsAircraft Type and Airworthiness Certificationrdquo Federal Avia-tion Regulations Federal Aviation Administration (1969)

29 Air-Conditioning Heating and Refrigeration Institute (AHRI)ldquoSound Quality Evaluation Procedures for Air-Conditioningand Refrigeration Equipmentrdquo Air-Conditioning Heating andRefrigeration Institute AHRIANSI 1140-2012 Air-ConditioningHeating and Refrigeration Institute (2012)

30 WE Blazier ldquoRevised noise criteria for application in theacoustical design and rating of HVAC systemsrdquo Noise ControlEngr 16(2) 64ndash73 (1981)

31 F Faul E Erdfelder A Buchner and A-G Lang ldquoStatisticalpower analyses using GPower 31 tests for correlation and re-gression analysesrdquo Behav Res Methods 41 1149ndash1160 (2009)

32 M Schutte A Marks E Wenning and B Griefahn ldquoThe de-velopment of the noise sensitivity questionnairerdquo Noise Heal9(34) 15 (2007)

33 J Francis J Lee A Steinbach and LM Wang ldquoDeterminingannoyance thresholds of tones in noiserdquo ASHRAE Trans120 (2014)

34 JW Grabbe ldquoSudoku and working memory performance forolder adultsrdquo Act Adapt Aging 35(3) 241ndash254 (2011)

35 HS Chang and JM Gibson ldquoThe oddndasheven effect in Sudokupuzzles effects of working memory aging and experiencerdquoAm J Psychol 124(3) 313ndash324 (2011)

36 ldquoNASA TLX Task Load Indexrdquo [Online] Available at httphumansystemsarcnasagovgroupstlx (accessed September 92015)

37 RFS Job J Hatfield NL Carter P Peploe R Taylor andS Morrell ldquoGeneral scales of community reaction to noise(dissatisfaction and perceived affectedness) are more reliablethan scales of annoyancerdquo J Acoust Soc Am 110(2) 939ndash946(2001)

82 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE

10 A Hastings H Lee P Davies and AM Surprenant ldquoMeasure-ment of the attributes of complex tonal components commonlyfound in product soundrdquo Noise Control Engr J 51(4) 195ndash209(2003)

11 EE Ryherd and LM Wang ldquoImplications of human perfor-mance and perception under tonal noise conditions on indoornoise criteriardquo J Acoust Soc Am 124(1) 218ndash226 (2008)

12 P Susini S McAdams S Winsberg I Perry S Vieillard andX Rodet ldquoCharacterizing the sound quality of air-conditioningnoiserdquo Appl Acoust 65(8) 763ndash790 (2004)

13 B Berglund P Hassmeacuten and A Preis ldquoAnnoyance and spectralcontrast are cues for similarity and preference of soundsrdquo J SoundVibr 250(1) 53ndash64 (2002)

14 U Landstroumlm E Aringkerlund A Kjellberg and M Tesarz ldquoEx-posure levels tonal components and noise annoyance in work-ing environmentsrdquo Environ Int 21(3) 265ndash275 (1995)

15 U Landstroumlm A Kjellberg and M Bystroumlm ldquoAcceptablelevels of sounds with different spectral characteristics duringthe performance of a simple and a complex non-auditory taskrdquoJ Sound Vibr 160(3) 533ndash542 (1993)

16 D Laird ldquoThe influence of noise on production and fatigue asrelated to pitch sensation level and steadiness of the noiserdquo JAppl Psychol 17(3) 320ndash330 (1933)

17 JV Grjmaldi ldquoSensori-motor performance under varying noiseconditionsrdquo Ergonomics 2(1) 34ndash43 (1958)

18 K Holmberg U Landstrom and A Kjellberg ldquoEffects of ven-tilation noise due to frequency characteristic and sound levelrdquoJ Low Freq Noise Vib 12(4) 115ndash122 (1993)

19 International Organization for Standardization (ISO) ldquoAcousticsmdashAssessment of noise annoyance by means of social and socio-acoustic surveysrdquo International Standard ISOTS 15666 2003International Organization for Standardization (2003)

20 TH Pedersen ldquoThe ldquoGenlydrdquo noise annoyance model dose-response relationships modelled by logistic functionsrdquo DELTAHoslashrsholm Danmark (2007)

21 CMarquis-Favre E Premat andDAubreacutee ldquoNoise and its effectsmdasha review on qualitative aspects of sound Part II noise and annoy-ancerdquo Acta Acust united with Acust 91(4) 626ndash642 (2005)

22 American National Standards Institute (ANSI) ldquoAcousticsmdashMeasurement of Airborne Noise Emitted by Information Technol-ogy and Telecommunications EquipmentmdashPart 1 Determinationof Sound Power Level and Emission Sound Pressure LevelrdquoAmerican National Standards Institute ANSIASA S1210-2010Part 1 American Society of America (2010)

23 W Aures ldquoThe sensory euphony as a function of auditory sen-sationsrdquo Acustica 58(5) 282ndash290 (1985)

24 American National Standards Institute (ANSI) ldquoProcedure forthe Computation of Loudness of Steady Soundsrdquo AmericanNational Standards Institute ANSI S34-2007 Acoustical Soci-ety of America (2007)

25 ldquoAcousticsmdashMethod for Calculating Loudness Levelrdquo Interna-tional Standard ISO 5321975 International Organization forStandardization (1975)

26 E Zwicker ldquoSubdivision of the audible frequency range intocritical bands (Frequenzgruppen)rdquo J Acoust Soc Am 33(2)248ndash248 (1961)

27 BR Glasberg and BCJ Moore ldquoPrediction of absolutethresholds and equal loudness contours using a modified loud-ness modelrdquo J Acoust Soc Am 120 585ndash588 (2006)

28 Federal Aviation Administration ldquoPart 36 Noise StandardsAircraft Type and Airworthiness Certificationrdquo Federal Avia-tion Regulations Federal Aviation Administration (1969)

29 Air-Conditioning Heating and Refrigeration Institute (AHRI)ldquoSound Quality Evaluation Procedures for Air-Conditioningand Refrigeration Equipmentrdquo Air-Conditioning Heating andRefrigeration Institute AHRIANSI 1140-2012 Air-ConditioningHeating and Refrigeration Institute (2012)

30 WE Blazier ldquoRevised noise criteria for application in theacoustical design and rating of HVAC systemsrdquo Noise ControlEngr 16(2) 64ndash73 (1981)

31 F Faul E Erdfelder A Buchner and A-G Lang ldquoStatisticalpower analyses using GPower 31 tests for correlation and re-gression analysesrdquo Behav Res Methods 41 1149ndash1160 (2009)

32 M Schutte A Marks E Wenning and B Griefahn ldquoThe de-velopment of the noise sensitivity questionnairerdquo Noise Heal9(34) 15 (2007)

33 J Francis J Lee A Steinbach and LM Wang ldquoDeterminingannoyance thresholds of tones in noiserdquo ASHRAE Trans120 (2014)

34 JW Grabbe ldquoSudoku and working memory performance forolder adultsrdquo Act Adapt Aging 35(3) 241ndash254 (2011)

35 HS Chang and JM Gibson ldquoThe oddndasheven effect in Sudokupuzzles effects of working memory aging and experiencerdquoAm J Psychol 124(3) 313ndash324 (2011)

36 ldquoNASA TLX Task Load Indexrdquo [Online] Available at httphumansystemsarcnasagovgroupstlx (accessed September 92015)

37 RFS Job J Hatfield NL Carter P Peploe R Taylor andS Morrell ldquoGeneral scales of community reaction to noise(dissatisfaction and perceived affectedness) are more reliablethan scales of annoyancerdquo J Acoust Soc Am 110(2) 939ndash946(2001)

82 Noise Control Engr J 65 (2) March-April 2017 Published by INCEUSA in conjunction with KSNVE