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Synthesis of Helicopters Noise for Sound Quality Analysis

Antonio Vecchio 1, Karl Janssens 2 and Christophe Schram 3 LMS International NV, Interleuvenlaan 68, Leuven, B-3001 Belgium

Jessica Fromell 4 KTH, Business Aeronautical and Vehicle Engineering KTH SE-100 44 Stockholm Sweden

and

Fausto Cenedese 5 Agusta-Westland, Viale G.Agusta 520, Cascina Costa, Italy

This paper presents a model-based sound synthesis approach for flyover and interior noise of aircraft, with special focus on helicopters. The research is aimed at the development of technology and tools to support a more environmental friendly design of modern helicopters thus facilitate a better exploitation of helicopters in regional transportation. The synthesis of helicopter noise is based on a Sound Quality Equivalent model that is extracted from sound recordings. A thorough noise analysis allows identifying and extracting the most relevant noise components. The sound synthesis model is then implemented as compact and sound quality accurate model consisting of a superimposition of rpm dependant tonal components, one third octave noise bands to characterize the broadband noise and interference pattern. The noise feature extraction process from recorded data and the sound synthesis are illustrated. The resulting noise models are validated for a number of typical helicopter sounds. The potential for using this technology approach to target sound design is briefly discussed.

Nomenclature RPM = Revolutions per Minutes, indicating the rotational speed regime of rotating machinery SQE = Sound Quality Equivalent DFT = Discrete Fourier Transform VCS /VHS = Virtual Car Sound / Virtual Helicopter Sound TVDFT = time Variant DFT DSP = Digital Signal Processing tacho = counter of RPM signal

I. Introduction oise pollution from air traffic is a one of the major environmental problems affecting many citizens [1,2]. Aircraft in general and particularly helicopters flyover noise represents an extremely complex auditory

scenario. Noise annoyance not only depends on sound exposure levels. There are also many other acoustic and psycho-acoustic factors such as spectral content, modulations, sharpness and tonality that play an important role.

This paper presents a model-based sound synthesis approach which takes these factors into consideration. The sound synthesis approach is interesting for a number of reasons: it allows studying and assessing noise annoyance in relation to the various acoustic and psycho-acoustic characteristics of the sound, it helps better understand sound

1 RTD Division Manager, Test Division R&D Department, Interleuvenlaan 68, Leuven - Belgium. 2 RTD Project Leader, Test Division R&D Department, Interleuvenlaan 68, Leuven - Belgium. 3 RTD Project Leader, CAE Division, Interleuvenlaan 68, Leuven - Belgium. 4 Research Engineer, Business Aeronautical and Vehicle Engineering KTH SE-100 44 Stockholm Sweden. 5 Acoustics and Vibration Specialist, Acoustics & Vibration Department - Agusta.

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13th AIAA/CEAS Aeroacoustics Conference (28th AIAA Aeroacoustics Conference) AIAA 2007-3707

Copyright © 2007 by LMS International. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.

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quality differences among various types of helicopters and flying conditions and it forms an excellent basis for target sound design.

The synthesis of aircraft noise is based on a compact and sound-quality-accurate sound synthesis model that is identified from noise recordings. In this respect, flyover noise is modelled from sound recordings measured near to the ground, interior noise from measurements performed on board of the aircraft during operating conditions. The approach takes advantage of the concepts and methods developed in previous research on interior car and aircraft sound [3,4,5].

II. Time-frequency analysis In order to better understand the

major characteristics of aircraft flyover noise, various recordings were analyzed in the time-frequency domain. Three major sound components can be recognized: a number of Doppler shifted tonal components, a broadband noise component and a typical interference pattern (Figure 1). The source generating such noise is dependant on the type of aircraft and sometimes on the specific flying conditions. Tonal noise is typically generated by jet engines components (fan and turbine noise). In case of helicopter noise also the main rotor and the tail rotors as well as the gearbox are responsible for tonal noise generation. In case of flyover noise, the source is moving hence Doppler effect will apply that causes frequency shift of tonal noise components.

The interference pattern is caused by a superposition of the direct sound and the reflections on the ground [6]. This is because the sound waves originating from the sources reach the measuring station following two path-ways: one is the direct transmission path from source to receiver; the second is the indirect path of sound waves that are reflected by the ground before they are captured by the microphone.

The shape of the interference pattern is determined by the time-delay between both sound contributions. This time-delay varies in function of time; it is related to the flight path and reaches a maximum value at the flyover point.

Analogously to what described for the exterior noise, a data driven analytical approach is used for modelling interior aircraft noise. In this case only fewer components are retrieved from the data structure, as Doppler effect and ground reflection interference patters are neglected. The result is that for interior noise of aircraft, a Sound Quality Equivalent Model can be achieved by superimposing only tonal components and background noise. However in case helicopter noise, such a simplified noise model does not correspond to a reduced technical challenge: the noise spectra exhibit very high tonal density, this especially at low frequency range. Noise models must hence be carefully validated against human listening test to ascertain that closely spaced tones are not missing in the noise synthesis and in case they are, this does not endanger the overall sound quality of the noise model. To this aim, psychoacoustic metrics and phenomena such as, amongst others, pitch, roughness and rumble are taken in consideration.

III. Sound modelling and synthesis In the generic case of aircraft flyover noise, the noise synthesis uses an accurate Sound Quality Equivalent model

(SQE). This refers to the capability of the model to reproduce the human perception of the noise under analysis. It is not the purpose of this technology approach to achieve high accuracy in reproducing the physics of all noise features as this would lead to synthesized sounds that would not adequately account for the relevant psychoacoustic effects of human perception. A “pure” physical synthesis of noise signal often results into the noise be perceived as

broadband noise

interferencepattern

Doppler shifted tones

broadband noise

interferencepattern

Doppler shifted tones

Figure 1. Time-frequency sound structure

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“artificial” or “synthetic”. In addition, this would require a far more intense computational load, making it more difficult on-line editing of noise components and re-synthesizing of the results, which is actually the most attractive aspect of the proposed approach.

For flyover noise, the Doppler shifted tonal components, the broadband noise and the interference pattern are the model components that most affect the perception in psychoacoustic terms. The identification of a sound synthesis model from a noise recording near the ground consists of four major steps as shown in Figure 2. The synthesis of interior noise is based on the same approach with the only difference that source data are recorded in the aircraft cabin and that exterior noise effects such as Doppler and ground reflection are neglected therefore not included into the sound synthesis model. Another relevant difference is the accuracy required to extract tonal components from sound recordings. While for aircraft flyover noise this is not a major technical difficulty – tones are clearly separated and thus easy to extract even when a reference rpm signal is missing - in case of helicopters this entails a major challenge. The rpm regime of a flying helicopter makes it such that the noise bandwidth affects lower frequency than for airplanes. Unfortunately this happen to be much more relevant to the human perception as lower frequency and closely spaced noise are prone to specific psychoacoustic phenomena that may seriously affect the way noise is perceived often resulting in a higher perceived noise annoyance. It is therefore mandatory to extract a very accurate phase and amplitude information for all tonal components in order to achieve a good quality sound synthesis model.

From the point of view of the noise feature extraction, closely spaced tones are the major characteristic of helicopter noise. Also there if a reference rpm recording is not available, tracking of tones results much more difficult.

Once all noise features have been accurately extracted, the model can be build. This result from the superposition of the different features and paves the way to simulation scenarios where each component can be easily modified to improve the accuracy of the Sound Quality Equivalent Model. This requires some optimization steps, including validation through jury testing.

When an accurate sound quality model has been achieved it can be used for target sound design. A SQE model then allows to easily introduce modifications in the sound components and evaluate the impact of such modification on the human perception. This process can be made on-line as the ‘noise synthesis engine’ is fully accessible in the Virtual Environment Sound, a prototype software environment were it is possible to virtually ‘drive’ a vehicle (from car to helicopters) and in the meantime listen to the resulting sounds and study the impact that different modifications may have on the sound quality.

The technology approach is thereby resumed. Sound perception is a very important part of product brand design, but sound perception can not be fully explained by physics because the human ear is a complex, non-linear device, with specific frequency dependent transmission characteristics. Next, psychological factors such as the culture, environment, circumstance, mind-set etc are equally responsible for building up the individual assessment of noise and its annoyance. The consequence is that for an early stage sound design an engineering environment where to model product sounds, modify sound components and listen to the resulting new sounds will pave the way for a faster and better focused target sound design. With this aim in mind the Virtual Environment Sound has been adapted and fine-tuned to the very specific requirements of helicopter noise. The resulting tool, the Virtual Helicopter Sound, is a simulation platform where noise recording form any helicopters can be analysed, decomposed and synthesized. The simulation power then takes advantage of a number of DSP techniques that allows on-line editing of sound components and on-line assessment of sound quality both in terms of subjective perception (listening test) and objective metrics (psychoacoustics).

A. Step 1: tracking and re-synthesis of tonal components In the first step of the approach the tonal components are tracked from the recorded sound. The tonal components can originate from various sources such as the fan or the turbine in the jet engine, or from specific phenomena like the buzz-saw noise that may occurs in some cases during aircraft take-off. In case helicopter sound

tracking and re-synthesis of tonal components

Step 1

modeling and re-synthesis of broadband noise in third octaves

Step 2

adding tonal components and third octave noise bands

Step 3

synthesized sound(s)

recorded sound(s)

characterization of ground-interference pattern

Step 4

Figure 2. Schematic representation of the sound modeling and synthesis approach

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is considered, also rotor blade noise and rotor vortex interactions are relevant noise generation mechanisms. For interior helicopter noise, the gearbox is an additional and very dominant source of tonal noise. All these tonal noise components have in common the periodicity of their time structure. This is strongly dependant on the RPM regimes of the rotating machine they relate to (jet engine, rotor, and gearbox). The number and the relative position of the tonal components in the noise spectra depends on the variations in the RPM regimes and, in case of flyover noise, on the relative speed between source and listeners (Doppler effect). The resulting frequency shifts affects the noise perception. Psychoacoustic phenomena such as tone masking effects, sharpness or roughness may arise that will lead to strong variations in annoyance. One of the basic requirements to model the noise is then the capability to extract tonal components, with a very accurate identification of amplitude and phase all along the duration of the noise recording, even when the considered tones are varying over time. This is the case for aircraft flyover (due to Doppler effect) but also for helicopter interior noise when run-up manoeuvres (and or manoeuvres with rpm variation) are considered. To this purpose, a semi-automatic method has been developed to track the frequency shift of the tonal components from the time-frequency spectrogram. The semi-automatic method consists of extracting the trend of a shifting tone in the spectrograms. This can be done by manually selecting few values in the spectrogram for a given tone and run a simple algorithm (e.g. spline interpolation) to fit the tone envelope (frequency evolution over time). Once this information is available, the amplitudes of the tonal components are estimated in function of time using the Discrete Fourier Transform (DFT). The DFT is applied to short time-segments in order to track the fast amplitude variations. This is needed to reproduce the sensation of sharpness at higher frequencies. The use of short time-segments also reduces the frequency smearing effects that are associated with the Doppler shift. Once the frequency behaviour and the fast amplitude variations of the different tonal components are characterized, they can be easily re-synthesized and auralized. The time-frequency spectrogram in Figure 3 shows the tonal component synthesis results for one of the aircraft sounds under study.

1. Tacho less rpm extraction for helicopter noise

For the automatic order tracking method to be applied, tacho pulse is a critical source of information as it allows extracting rpm evolution over time. Unfortunately, a reliable tacho trace is not always available. This can be due to problems encountered at testing stage – the probes get loose or lost, or there could not be found a solution to safely mount a tacho probe – or to data processing problems. In these cases, a method to extract the rpm without a tacho signal is of high value as it allows running a number of post processing techniques with good accuracy level. In the case under analysis, a dominant order is tracked in the time domain. Knowing the order number and its position in the spectrogram, the rpm can be

0 7000Hz

44

0

s

dB60

-400 7000Hz

44

0

s

dB60

-400 7000Hz

44

0

s

dB60

-40

Figure 3. Time-frequency spectrogram of the tonal component

Figure 4. Multiple points tracking in tacho-less rpm extraction

h d

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estimated. The estimated rpm profile can be used at a later stage to generate a tacho pulse, which can then be used in the automatic order tracking.

A dominant order is tracked by manually selecting multiple points (Figure 4). First a linear curve fit is made between the selected points, which serve as an initial estimate of the fundamental frequency. This is followed by peak value detection in a frequency band centred on this initial estimate. This peak detection algorithm calculates a zoomed DFT and searches for the highest dB value in the specified frequency band (Figure 5).

From this predicted curve the rpm is calculated. The time signal is divided into a series of small overlapping time segments and for each time segment the fundamental frequency is found.

The above described approach was used to synthesise the helicopter noise, but the results showed that for helicopter noise, the inter-order phase relations are very important to correctly reconstruct sound perception. Missing such information leads to a reduced sound quality model. The semi-automatic method was found indeed very well fit for aircraft flyover noise, but for helicopter noise it resulted very time consuming and error prone due to the high tonal density at low frequency range. A more efficient method was therefore required.

Another approach was used instead, where the orders are automatically tracked with the help of RPM information. The approach is called automatic order tracking because it allows tracking orders and / or tones in a noise signal by using the rpm envelope of the noise signal suitably extracted from a tacho channels (e.g. pulse train). The method was then adapted and further developed to satisfactory synthesize helicopter noise. As this method operates in the time/order domain, the noise signal needs to be first suitably transformed from its current time/frequency domain to the order domain. This process involves adaptive resampling and Time Variant Discrete Fourier Transform (TVDFT), both methods will be shortly described. 2. Adaptive Resampling

Adaptive or synchronous resampling allows modifying the sampling rate of a signal in such a way that it can be analysed in a different domain. In the present work, adaptive resampling targets to synthesize the sound in the order domain, because orders (i.e. helicopter tonal noise) are the objective of the signal extraction process.

In automotive applications, the extraction of “order-related” phenomena of e.g. engine vibrations based on the measurements of the rotational speed of one of its components is a well consolidated technique, but the application to helicopter noise poses some specific technical challenge.

At high frequency regimes, the frequency domain is very sensitive to even small variations in rpm. Measurements taken on an engine mount at - supposedly - constant rpm where very slight variations in rpm occur will result in a frequency domain signal representation where the tonal components are sharp at low frequency range, but become smeared out for higher frequencies. Hence small rpm variations may introduce leakage errors in the frequency domain.

For applications like helicopter noise analysis where there is a need to investigate higher order phenomena, the smearing in the frequency domain makes it very difficult to discriminate order from resonance components.

Adaptive resampling brings the signal representation in the order domain, where all orders appear very clearly, hence reduces the risk of inaccurate tone extraction. On the other hand resonance phenomena may get smeared out, but this is less relevant for the synthesis hereto envisaged.

Figure 5. Predicted curve in tacho-less rpm extraction method

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To apply adaptive resampling, the original time signal must be measured synchronously with a tracking signal such as a tacho signal or a pulse train, which is then converted to an rpm/time function and thereafter integrated to obtain an angle/time function.

In the transformation from the time to the angle domain, the requested (constant) resolution in the angle domain (∆α) defines the time intervals at which data samples are required in the measured signal.

The most appropriate resolution (∆α) is based on the minimum slew rate which must be coped with. When sampling in time domain, the time increment is the reciprocal of the sampling frequency, equation (1).

(1) TFs

∆=1

According to the Nyquist criterion, this fixes an upper bound to the availability of signal samples in the measured data. The same rule applies to adaptive resampling, hence if the requested angle resolution is very high, the time signal must be upsampled to generate required the sample resolution, this may entail high computation costs. On the contrary, if the requested angle resolution is very low, the time signal will be decimated, causing a loss of information that may be critical for the sound synthesis.

The ∆α corresponding to the required Fs has to be determined in the time/frequency domain. Adaptive resampling uses a varying time increment if the angle/time relationship is not linear. Data loss will occur first at the lowest rpm values and the aim is to determine the ∆α corresponding to the best compromise between over and under sampling.

(2) ss F

rpmF

dtd

minmin =

=∆

α

α

Using an angle increment less than the value given by equation (2) represents excessive processing. Using a higher increment value results in a loss of information in the lower rpm ranges, which will not be recovered if the data is transformed back to its original domain.

Once the optimal angle resolution has been set, the signal can be reconstructed in the angle domain. Interpolation may be required depending on the resolution of the original signal and the relation between both domains. To preserve the signals spectral contents, which are very important, it is first upsampled before being interpolated. Finally the constructed angle-domain signal needs to be resampled, usually down-sampled, to match the angle resolution that is desired.

If correctly applied, adaptive resampling is a fast and useful tool to convert a signal from one domain to another.

3. Time VariantDFT The time variant discrete Fourier transform is a DFT whose frequency varies as a function of time. This is

specifically done to allow DFT algorithm focus on the signal time frame corresponding to a specific rpm condition. The TVDFT hence takes full advantage of the adaptive resampling as it can rely upon the appropriate time segment with the appropriate sampling ratio. The algorithm allows extracting the amplitude and phase information as a function of rpm.

The method is based on the transform in equations (3) and (4) where the kernel is a cosine or sine function of unity amplitude with an instantaneous frequency matching that of the tracked order at each instant in time. The kernel may also be formulated in a complex exponential form similar to the corresponding Fourier transform.

(3)

∆∆= ∑∑

∆

=

dtrpmtonxN

a n

N

nn )60/* *(2t)cos(1 tn

01

π

(4)

∆∆= ∑∑

∆

=

dtrpmtonxN

b n

N

nn )60/* *(2t)sin(1 tn

01π

This transform is best suited for estimation of orders with constant order bandwidth which is obtained by performing the transform over the number of time points required to achieve the desired order resolution. The order resolution is defined as the inverse of the number of revolutions in the analysis block. This implies that as the rpm increases, the transform will be applied over a shorter time, giving a wider ∆f. This behaviour has been found to be advantageous for order tracking.

Since the frequency of the kernel of this transform matches the frequency of the order of interest at each instant in time, there is no leakage due to the order not falling on a spectral line. There will, however, be leakage effects

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from other orders, not tracked, that are present in the data. These orders can “leak” into the frequency band of analysis around the order. Windows typically used for conventional FFT analysis are also used with this transform. Since all windows have a frequency resolution/amplitude estimate trade-off, the chosen window can have a significant effect on the results.

Which window to use is chosen by the user dependant on the order content of the data and the aspects of the order estimate the user deems most important.

For order tracking the TVDFT method is very practical and it contains many of the advantages of the resampling based algorithms without much of the computational load and complexity.

B. Step 2: modelling and re-synthesis of broadband noise in third octaves The second step of the approach concerns the modelling and re-synthesis of the broadband noise in third octave

bands. The broadband noise originates from different sources. The most important ones are the jet noise and airframe noise.

To model the broadband noise, the tonal components are first subtracted from the original sound in the time-frequency domain. Then the broadband noise is decomposed in third octave noise bands with varying level in function of time. Once this is achieved, the broadband noise is re-synthesized and auralized.

At very low frequency and correspondingly for low orders and low rpm regimes, the subtraction of tones from the noise is rather difficult as tonal density is such that inter-tonal noise gets filtered out together with tones.

To improve the model of the background noise a new method was developed were a so called noise floor is calculated by finding the local minima in the recorded noise. The sound is divided in small elements and a local minima value is then found for each of these elements which is then stored in the matrix of the noise floor. This noise floor is thereafter retrieved from the noise matrix and replaced in those gaps observed in the noise synthesis where the extraction of too closely spaced tones caused loss of any noise information. The resulting background noise is then modelled in third octave bands, and provides a better noise synthesis as it allows reconstructing interorder signal information more accurately. The broadband noise is then decomposed in third octave noise bands with levels varying as a function of rpm. Finally this broadband noise is re-synthesized and auralized. Figure 6 shows the spectrogram of the rpm-frequency

domain noise resulting from the above described process. The source noise is the recorded on the helicopter while the main rotor is running. For sake of completeness, it must be added that applying the noise floor method over the entire noise recording is not a wise approach as in case of largely spaced tones, the local minima method leads to incorrect amplitudes estimations. These would results in genera too low, for sure lower than the actual level in the recorded sounds. The method is best suited for closely spaced tones, where the distance separated tones is below 10Hz.

In run-up were orders do not have constant width, some orders can be partly missing or background noise may be over-subtracted. This might introduce smearing effect in the background

noise or result into a synthesized signal having too little energy between the orders. This problem is minimized with a subtraction process were the width of the subtracted order is dependant of the rpm and the frequency.

The noise floor method was successfully validated on a stationary helicopter signal with very closely spaced order components.

Figure 6: Waterfall of rest noise matrix

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C. Step 3: signal reconstruction, adding tonal components and third octave bands To reconstruct the time signal, the extracted and synthesized noise features are added up. For aircraft flyover

noise this refers to the synthesized Doppler shifted tonal components, the third octave bands representing the background noise. For the Helicopter noise this only reflect to the combination of rpm dependant tonal noise and background noise. The resulting sound represents the helicopter noise synthesis; a spectrogram of the synthesis signal is shown in Figure 7. For aircraft flyover noise model, this reconstruction only refers to the noise in free field conditions, still missing is the contribution of the ground interference pattern. This needs to be added to the model as it strongly affects the overall sound character and its perception.

A comparison between the synthesized (Figure 7) and the original measured sound (Figure 8) show that the synthesis produces a good physical representation of the original sound. Rpm evolution as well as tone amplitude is correctly retrieved in the sound synthesis. However, dedicated listening tests revealed some minor differences in noise perception. First occasional modulations are perceived in the synthesized signal, mainly occurring at low rpm regimes that are not present n the original sound. Second, a small rpm mismatch is to be reported, that is however not audible.

A closer analysis was then performed to identify possible source of errors and inaccuracy. This led to a critical review of the processing steps and pointed out that a valid tacho trace is essential for high accuracy results.

In the case under observation the extraction of tonal components had to be carried out using the tacho-less method. This was mandatory as no physical tacho signal was available with the test data. As already described this partly relies on manual selections of points in the spectrogram and then to a curve fitting approximation (spline interpolation). The result is that a small difference in the rpm evolution over time must be reported. Next, the small inaccuracy on the rpm-time correspondence will entail some inaccuracy in the automatic order tracking method. This is because rpm-tracking is very sensitive to the kernel frequency of the TVDFT, thus a small difference in the rpm profile will

Figure 7: Rpm-frequency spectrogram of the synthesized helicopter noise

Figure 8: Rpm-frequency spectrogram of the original helicopter noise

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lead to inaccurate amplitude estimation of the given tonal component. The second aspect is that a correct estimation of the inter-order phase relations is simply missing and all orders/tones have the same phase evolution (Figure 9. The first inaccuracy will have effects on the time evolution of the sound, the second an even worst effect on the inter-tonal noise reconstruction (e.g. modulation effects).

D. Step 4: characterization of ground interference pattern The fourth and last step of the approach considers the ground-interference pattern. A method has been developed

to track the interference pattern from the time-frequency spectrogram of the recorded sound (Figure 1). From the amplitudes and frequencies of the peaks and troughs, one can estimate the reflection coefficient and time-delay between the direct incident and reflected sound. These are the most important parameters that characterize the interference pattern. Both parameters are varying in time and reach a maximum value at flyover point.

Once the ground-reflection coefficient and time-delay are known, the free field flyover sound, synthesized in the previous steps of the approach, can be decomposed into a direct and reflected sound component. The sum of both sound components gives the final synthesized sound at the considered microphone position near the ground. The decomposition process is schematically presented in Figure 10.

IV. Target sound design: The Virtual Helicopter Sound Environment A software environment was developed were it is possible to virtually drive a car (or in this case fly an aircraft) and in the meantime study the impact different modifications have on the noise perception and the sound quality. The tool was first designed for interior car sounds, but some modifications were implemented to cope with aircraft and helicopter noise. Some of the tool’s features are very relevant for the case under study: once the fundamental noise components have been suitably modelled, the sound can be synthesized and replayed according to any rpm evolution. Sound synthesis can then be recorded and stored on a data file. This makes it possible to develop a processing technique that allows improving the noise synthesis quality also for sounds where an accurate rpm reference is missing. The tool was intensively used to investigate to what extent not having a proper tacho pulse signal hampers the achievement of a good quality helicopter noise synthesis.

First a slow run up sound was recorded in VHS using a noise model derived from a data set missing a tacho signal. Thereafter the sound synthesis was replayed in VHS while the tacho was recorded. In this respect a sound with only one order is used as tacho signal. The order chosen for such purpose is dependant on order that is targeted in the phase tracking process. VHS produces both the sound synthesis and its rpm, these can thereafter be exported to text files and used to realize a new synthesis model. The ‘virtually measured’ sound and the tacho can then be shifted in time such as to improve the time signal match. This actually was the biggest problem in the tacholess

11+α

1+ααtime-

delay

+

direct

reflected

1.2 m above the ground free field

Figure 10: Decomposition scheme to characterize ground-interference pattern.

Figure 9: Order extraction using tacholess process: missing inter order phase relation.

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extraction process and is thereby suitably recovered. When such time shift is applied, the sound can be synthesized again, now with the rpm profile and the corresponding trigger pulse information that was missing in the original synthesis model. A new model can then be implemented that accounts for the correct interorder phase relations and

hence removes the noise perception inaccuracy related to the modulation effects at low rpm (Figure 11). The process can be made iterative and dependant on the level of accuracy that is required.

Once sound-quality-accurate results are achieved, the sound synthesis model can be used for target sound design. The advantage of the VHS environment are here evident: one can easily modify model components and evaluate the impact on the human perception. A large variety of so-called “what if” scenarios can be played. For example,

what happens if the dominant tonal components are reduced with 3 dB? Or what is the sound quality impact if the levels of some low or high frequency third octave noise bands are changed? Or what happens with our sensation of sharpness when the rapid amplitude variations of the high-frequency tonal components are smoothed? Or what happens with the ground-interference pattern and our sound perception if the surface characteristics around the heliport are changed? By playing some of these “what if” scenarios, one can design target sounds with improved sound quality and suggest engineering guidelines for future aero-acoustic improvements.

Figure 11: Order extraction using VHS: reconstructed inter order phase relation.

Figure 12: Virtual Helicopter Sound Environment

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V. Conclusion A sound synthesis approach was developed for helicopter noise. The noise synthesis is based on a compact and

sound-quality-accurate model which is identified from measured noise data. A sound synthesis model consists of a number of tonal components and third octave bands that describe the broadband noise. The ground-reflection coefficient and the time-delay between the direct and reflected sound are also taken into account to characterize the typical interference pattern peculiar of flyover noise. For the few aircraft sounds studied so far, impressive synthesis results were achieved. Almost no differences could be heard between the synthesized and measured sounds.

In case the helicopter noise, the quality of the noise synthesis relies on a very accurate estimation of the rpm regime. This is best extracted when tacho signal is available with the data. However, an alternative processing technique based on the use of the Virtual Helicopter Sound Environment has been described that allows improving to a relevant extent the overall quality of the noise synthesis even in case of missing or inaccurate rpm information.

The presented approach paves the way for a faster and accurate target sound design also in case helicopters noise is considered. Acoustic engineers can easily modify model components and assess the impact on the human perception. This way they can design target sounds with improved sound quality and suggest guidelines for future design improvements. The sound synthesis helps better understand sound quality differences among various types of helicopters manoeuvres and forms an excellent basis to design target sounds with improved noise signature.

Acknowledgments The present research work was carried out in the frame of the FP6 research project FRIENDCOPTER. The

support of the European Commission is gratefully acknowledged.

References Books

2Smith M.J.T. Aircraft noise. Cambridge University Press, MA, 1989, pp. 359. Proceedings

3Janssens K., Coomans H., Belghit I., Vecchio A., Van de Ponseele P., Van der Auweraer H. “A virtual sound synthesis approach for on-line assessment of interior car and aircraft noise”. ICSV Conference, St. Petersburg, Russia, July 2004.

4Van de Ponseele P., Adams M., Janssens K., Vallejos L. “A virtual car sound environment for interactive, real-time sound quality evaluation”. Internoise Conference, Dearborn, US, 2002.

5Vecchio A., Janssens K., Belghit I., Scheers W. “A virtual sound synthesis environment for on-line assessment of aircraft interior noise: prototype testing simulation and real-time sound quality analysis”. Internoise Conference, Prague, Czech Republic, August 2004

6Smith A., Hayward S., Rich N. “Perceptions of aircraft noise exposure, noise sensitivity, sleep disturbance and health: results of the Bristol noise, sleep and health study”. Internoise Conference, Nice, France, 2000.

7J. Fromell, “Synthesis of Helicopter Interior Noise for Sound Quality Analysis”. Master Thesis for the Diploma of 8 J.Blough, D.Brown - The time variant discrete Fourier transform as an order tracking method.