Romblom
Overview
Non-IdealReverberation
DFM: Algorithm
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Diffuse Field ModelingThe Physical and Perceptual Properties of Spatialized
Reverberation
David Romblom1,2
1Music Technology, Schulich School of Music, McGill University
2Centre for Interdisciplinary Research in Music Media and Technology (CIRMMT)
November 2016
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Romblom
Overview
Non-IdealReverberation
DFM: Algorithm
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Extra Stuff
Overview
1 Overview
2 Non-Ideal Reverberation
3 DFM: Algorithm
4 Contributions
5 Extra Stuff
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Romblom
Overview
Non-IdealReverberation
DFM: Algorithm
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Spatial Audio
Goal - natural and immersive virtual auditory experiences.
Major research areas:
Perception of auditory space.Signal processing.Room acoustics and electro-acoustics.
Applications:
Music.Perceptual research.Cinema, gaming, and virtual reality.Telecommunications.Hearing health.
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DFM: Algorithm
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Research Directions
Focus: spatial aspects of reverberation.
Starting point - perceptual evaluation of existing techniques.
Hamasaki Square.
Perception of non-ideal diffuse field reverberation.
Is the auditory system sensitive to this?Can such differences be found in existing acoustic spaces?
Diffuse Field Modeling Algorithm.
Approximate reverberation based on known physical acoustics.
Systematically create room effect for large loudspeaker arrays.
Validation - Is this algorithm usable in practice?
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Non-IdealReverberation
DFM: Algorithm
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Room Acoustic Basics
Direct path from source to observer.
Early reflections (ER) - geometrically modeled using image /source technique.
Diffuse field - many simultaneous reflections.
Usually described statistically.
this → perception, modeling, and reproduction ofreverberant diffuse fields.
Direct path and early reflections - VBAP and WFS.Complementary to existing toolkit.
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Background: Microphones and B-Format
Pressure microphone → omni-directional.
Pressure gradient → bi-directional (Figure-8.)
Omni + Figure-8 → Cardioid (with proper coefficients).
B-Format:
Pressure and x,y,z pressure gradient - W,X,Y,Z.”Matrix” to form cardioid microphones.
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Evaluation of Existing Techniques
12/2: Frontal wavefront reconstruction, surrounding ambience.
“Acoustic curtain” - cardioids mapped directly to loudspeakers.
David Romblom, Richard King, and Catherine Guastavino “A Perceptual Evaluation of RoomEffect Methods for Multichannel Spatial Audio,” 135th Convention of the Audio EngineeringSociety, October 2013. “Best Student Technical Paper” (peer-reviewed).
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DFM: Algorithm
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Hamasaki Square
Hamasaki Square:
Bi-directional microphones routed directly to L/R/LS/RS.Null oriented to stage, positive lobe to the walls.Spacing controls the channel correlation.
Adapted to 12/2 - L/R as virtual WFS sources.
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Non-IdealReverberation
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Ratings for the Distance Scale (Near/Far)
SPOT,DRY ARRAY,DRY ARRAY,SURR SPOT,HS ARRAY,HS0
10
20
30
40
50
60
70
80
90
100
Treatments
Mean R
ating
All Excerpts Vibraphone Forte Piano Percussion
ARRAY,HS > All other cases.
Commensurate reverberation → Direct to Reverberant ratio (D/R) isn’t thewhole story.Likely dependence on early reflections.Possible dependence on channel correlation.
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Ratings for the Envelopment Scale (Low/High)
SPOT,DRY ARRAY,DRY ARRAY,SURR SPOT,HS ARRAY,HS0
10
20
30
40
50
60
70
80
90
100
Treatments
Mean R
ating
All Excerpts Vibraphone Forte Piano Percussion
ARRAY,HS and SPOT,HS > All other cases.
Decline interpretation in terms of D/R given commensurate reverberation.Likely dependence on frequency-dependent channel correlation.
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Overview
Non-IdealReverberation
DFM: Algorithm
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Extra Stuff
Non-Ideal Diffuse Field Reverberation
1 Overview
2 Non-Ideal Reverberation
3 DFM: Algorithm
4 Contributions
5 Extra Stuff
David Romblom, Catherine Guastavino, and Philippe Depalle “Perceptual Thresholds forNon-Ideal Diffuse Field Reverberation,” Accepted to the Journal of the Acoustical Society ofAmerica, October 2016.
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Non-IdealReverberation
DFM: Algorithm
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Research Questions
A diffuse field is many plane waves from all directions.
The non-ideal case has directional dependence.
This might be expected in:A partially enclosed room.A room with substantially different wall materials.Virtual spaces.
Is the auditory system sensitive to these energetic directionaldifferences?
Can we find commensurate differences in existing acousticspaces?
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Loudspeaker Segments
Meters-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5
Me
ters
-3
-2
-1
0
1
2
3
Lateral, height, and frontal conditions.
Vary SPL of a segment while maintaining overall power.
Loudspeakers driven by uncorrelated, temporally shaped noise.
Discrimination test (ABX).
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Lateral Results
stimulus intensity dB0.125 0.375 0.875 1.875 3.875 7.875
pro
port
ion c
orr
ect
0.4
0.5
0.6
0.7
0.8
0.9
1Lateral Condition
Psychometric functions fit per-subject using Palamedes toolbox.Guess rate 50%, maximum lapse rate 0.06%.Goodness of fit - Monte Carlo deviance distribution (p = 0.05).
Over all participants, the threshold is 2.5 dB for lateral condition.
≈ 6.8 dB for height; ≈ 3.2 dB for frontal.
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Can We Find This In Existing Spaces?
Thresholds correspond to loudspeaker levels.
Within-apparatus measurements differ from this.
Stimulus level immediately above perceptual threshold.
Difference of power spectral density for opposing cardioidmicrophones.
Similar differences found for height and frontal.
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Non-IdealReverberation
DFM: Algorithm
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Conclusions
Non-ideal diffuse field reverberation is audible.2.5 dB for the lateral,6.8 dB for height,3.2 dB for frontal.
Such level differences can be found in existing acoustic spaces.Within-apparatus level differences corresponding to the perceptualthresholds.
May be relevant in spatial impression.
Should be preserved in sound reproduction.
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Overview
Non-IdealReverberation
DFM: Algorithm
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Extra Stuff
Diffuse Field Modeling Algorithm
1 Overview
2 Non-Ideal Reverberation
3 DFM: Algorithm
4 Contributions
5 Extra Stuff
David Romblom, Philippe Depalle, Catherine Guastavino, and Richard King “Diffuse FieldModeling Using Physically-Inspired Decorrelation Filters and B-Format Microphones: Part IAlgorithm,” Journal of the Audio Engineering Society, vol. 64, pp. 177 - 193 (April 2016).
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Overview
Non-IdealReverberation
DFM: Algorithm
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Diffuse Field Modeling
Why?
Techniques exist to record and reproduce point sources.Hamasaki Square is great, but it is only for 5.1 surround.Need for a systematic tool for diffuse sound fields.
How?
Virtual array of outward-facing cardioid microphones.Real array of inward-facing loudspeakers.Coincident location links recording and listening spaces.Ensemble of decorrelation filters simulates structure of diffusefield.
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DFM: Algorithm
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Spatial Reverberation: Hamasaki Square
Four bi-directional microphones in a recording space directlyrouted to four loudspeakers in a listening space.
Defines a (very sparsely) sampled surface.
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Coincident Microphone / Loudspeaker Array
Outward-facing microphone.Inward-facing loudspeaker.Transducer pairs define a surface.
Meters-3 -2 -1 0 1 2 3
Mete
rs
-3
-2
-1
0
1
2
3
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Non-IdealReverberation
DFM: Algorithm
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Kirchhoff Helmholtz Integral
K/H formalizes the concept of the array:
p(r) = −∫∫
S
(∇np G (rs)− p∇nG (rs)
)−→dS (1)
Two “layers”:dipole microphone driving a monopole radiator.monopole microphone driving a dipole radiator.
Define cardioid to cardioid approximation:
p(r) ≈ − jk
2
∫∫S
(p +−∇np
jk
)[G (rs) +
∇nG (rs)
jk
]−→dS (2)
One “layer”:cardioid microphone driving a cardioid radiator.
r - point in inside space; rs - local vector; ∇n - surface normal gradient.
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Non-IdealReverberation
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Diffuse Field Simulation and Reconstruction
160 Hz using Q = 1024 plane waves of random direction andphase.
Left - reference simulation;Center - double-layer K/H using measured edge values.Right - cardioid to cardioid approximation with measured edge values.
16 spatial sampling points.
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Spatial Distribution and Autocorrelation
In a diffuse field:Mean squared pressure p2 is exponentially distributed.Phase is uniformly distributed on the interval [−π π [.
Spatial autocorrelation describes how pressure p changes atother locations ∆r for spatial frequency k .
Lower frequencies will be more correlated for greater distances.
〈p(r)p(r + ∆r)〉 = 〈p2〉 sin k |∆r |k |∆r |
(3)
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RT60 and Frequency Autocorrelation
Room modes superimpose in both frequency and space.
The spectrum at every point in space is different.
Long RT60 → ringing modes → narrow bandwidth → jaggedspectrum.
Short RT60 → damped modes → wide bandwidth → smoothspectrum.
Described by the frequency autocorrelation:
〈p2(ω)p2(ω + ∆ω)〉 = 〈p2(ω)〉2[
1 +1
1 + (τ∆ω)2
](4)
τ is the modal decay time; RT60 = 6.9τ .
(τ∆ω)2 is large; RH term is near 1.0.
(τ∆ω)2 is small; RH term is near 2.0.
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How To Use This Information
“Transpose” the matrix’d B-Format cardioid microphone to theedge of the array.
Decorrelation filters simulate measurements in a diffuse field.
The simulated array measurements should have appropriatedistributions and (spatial) autocorrelation.
Frequency autocorrelation of the ensemble of filters ≈ frequencyautocorrelation of the RIR (∝ to RT60).
How:Filters synthesized using a “Bank of Bandpass” structure.Random but structured gains per bandpass filter.
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Gated Exponential Bandpass Filters
Gated exponential bl [n]:
bl [n] = e−(1/νl−jΩl )n u[n] (5)
Decay constant νl and center frequency Ωl
Time0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
Amplitude
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1200 Hz, time constant 100 ms
Frequency50 100 150 200 250 300 350 400 450 500
dB
-10
-5
0
5
10
15
20
25
30
35
40
Short decays → wide bandwidth.
Long decays → narrow bandwidth.
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Bank of Bandpass Structure
L evenly-spaced bandpass filters are superimposed per channel i :
di =L∑
l=1
Ai,l bl (6)
The superimposed frequency response is:
Di (m) =L∑
l=1
Ai,lBl(ejΩm) (7)
Ai,l is a random complex gain with:Magnitude squared from an exponential distribution.Uniform distribution phase distribution.
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Spatial Filtering
Desired that Ai,l have the correct spatial autocorrelation withrespect to the I channels (space).
Lowpass filter Ai,l across i prior to superposition.
Power Spectral Density of Ai,l is uniform.Wiener Khintchine Theorem - PSD inverse transforms to autocorrelation.
64-channel (left); 16-channel (center); 64-channel array plotted wrt kr .
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Tuned Frequency Autocorrelation
Γ(m) is the frequency autocorrelation of Di (m) for the ensembleof filters I . Desired that:
Γ(m) ≈ 〈p2(ω)p2(ω + ∆ω)〉〈p2(ω)〉2
(8)
Overlapping band edges of Bl(m) smooth the spectrum.
Adjust decay constant νl to achieve correspondence.
Frequency0 100 200 300 400 500 600
dB
-10
-5
0
5
10
15
20
25
30
35200 Hz and 300 Hz, time constant 100 ms
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Simulated Fields - Visualization
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Spatial Autocorrelation of Simulated Fields
meters
0.5 1 1.5 2
βm(r)
-1
-0.5
0
0.5
1Autocorrelation 40 Hz
meters
0.5 1 1.5 2
βm(r)
-1
-0.5
0
0.5
1Autocorrelation 80 Hz
C2CNoneDFM
meters
0.5 1 1.5 2
βm(r)
-1
-0.5
0
0.5
1Autocorrelation 160 Hz
meters
0.5 1 1.5 2
βm(r)
-1
-0.5
0
0.5
1Autocorrelation 320 Hz
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Results - Algorithm
Kirchhoff Helmholtz Integral substantiates the virtual array.
Channel correlation converges to theoretical ideal.
Filter structure allows control of frequency autocorrelation.
Two-dimensional MATLAB simulation:
Visualization of complex-valued fields.Validation of correct spatial autocorrelation.
Matrix’d B-Format cardioid microphones preserves broadenergetic differences.
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Results - Evaluation
Experiment 1:
Room effect for an array of 20 loudspeakers.
11 of 16 expert SR engineers believe that DFM usable.
Use DFM with geometrically modeled reflections.High ratings for timbral quality, and spatial quality.Verbal data associated with “natural” and “being there.”
Use all four components of B-Format RIR.Low rating of 0th-order treatment (significant).
Experiment 2:
DFM compares well with the Hamasaki Square in 5.1.Similar ratings for “Depth of Stage” and ”Overall Quality.”
Olli Rummukainen, David Romblom, and Catherine Guastavino “Diffuse Field ModelingUsing Physically-Inspired Decorrelation Filters and B-Format Microphones: Part IIEvaluation,” Journal of the Audio Engineering Society, vol. 64, pp. 194 - 207 (April 2016).
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Non-IdealReverberation
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Extra Stuff
Contributions
1 Overview
2 Non-Ideal Reverberation
3 DFM: Algorithm
4 Contributions
5 Extra Stuff
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Contributions
Evaluation of Existing Techniques:
The Hamasaki Square has high ratings for distance and envelopment.
Non-ideal diffuse field reverberation:
Is audible.
Similar energetic differences can be found in existing acoustic spaces.
Diffuse Field Modeling:
The structure of a diffuse field can be simulated using decorrelationfilters.
Many channels of reverberation can be generated for large,regularly-spaced arrays.
DFM has been validated as usable for both large and commonloudspeaker arrays.
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Future Work
Non-ideal diffuse field reverberation:
Perceptual testing including the direct and early reflections.
Relate to physical properties of the room.
Diffuse Field Modeling:
Improvements to the DFM filter design.
Binaural adaption of DFM amenable to head-tracking.
Open source MATLAB toolbox.
Compare with physical measurements.
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Thank You!!
Thank you for your time and attention.
Thank you to our collaborators and supporters:
Sennheiser SFTC (Ambeo)Audiokinetic (wWise)Applied Acoustic Systems (Tassman)Natural Sciences and Engineering Research Council of Canada(NSERC).Centre for Interdisciplinary Research in Music Media andTechnology (CIRMMT).
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Publications
David Romblom, Richard King, and Catherine Guastavino “A Perceptual Evaluation of Room
Effect Methods for Multichannel Spatial Audio,” 135th Convention of the Audio Engineering
Society, October 2013. “Best Student Technical Paper,” (peer-reviewed).
David Romblom, Catherine Guastavino, and Philippe Depalle “Perceptual Thresholds for
Non-Ideal Diffuse Field Reverberation,” Accepted to the Journal of the Acoustical Society of
America, October 2016.
David Romblom, Philippe Depalle, Catherine Guastavino, and Richard King “Diffuse Field
Modeling Using Physically-Inspired Decorrelation Filters and B-Format Microphones: Part I
Algorithm,” Journal of the Audio Engineering Society, vol. 64, pp. 177 - 193 (April 2016).
Olli Rummukainen, David Romblom, and Catherine Guastavino “Diffuse Field ModelingUsing Physically-Inspired Decorrelation Filters and B-Format Microphones: Part IIEvaluation,” Journal of the Audio Engineering Society, vol. 64, pp. 194 - 207 (April 2016).
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Romblom
Overview
Non-IdealReverberation
DFM: Algorithm
Contributions
Extra Stuff
Extra Stuff
1 Overview
2 Non-Ideal Reverberation
3 DFM: Algorithm
4 Contributions
5 Extra Stuff
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Magnitude Rating Scales
Semantic Scale DefinitionImage distance The distance that the ensemble or solo instru-
ment is perceived to be at.Image depth A range of distinct distances within ensemble’s
or solo instrument’s image.Sense of Envelopment The sense of being inside an acoustic space.Localization The precision of localization of the ensemble or
solo instrument.Image Stability The stability of the image to head or body move-
ments.Instrument Width The width of the ensemble or solo instrument.Coloration / Transparency The spectral coloration of the ensemble or solo
instrument.
Scene-based paradigm (Rumsey, 2001.)
Emergent attributes from written response (Guastavino and Katz, 2004.)
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Details of Room Effect Experiment
Excerpts were taken from:
A modern vibraphone piece,A modern percussion ensemble, and,A classical piano sonata played on a period instrument.
Spot recordings were made to match the array recordings asclosely as possible (panning and geometric delays.)
Method Details:A trial consisted of:
A single musical excerpt,A single semantic scale,A number of treatments and their corresponding rating scales.
ANOVA on unnormalized ratings.Outliers discarded at two standard deviations (3% of the data).Post-hoc tests: Tukey-Kramer multiple comparisons at (p < 0.05).
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Treatments and Mixing
Alternate room effect:Hall-facing cardioid microphones mounted on main array.10 ms delay (MMA electronic timing offset).
Treatment TechniqueSPOT,DRY Spots microphones, SE WFS processor, no
room effect.ARRAY,DRY Acoustic curtain, no room effect.ARRAY,SURR Acoustic curtain, cardioid room microphones
with ETO.ARRAY,HS Acoustic curtain and Hamasaki Square.SPOT,HS SE WFS processor and Hamasaki Square,
1.5dB gain increase L/R HS.
Mixing notes:ARRAY,HS, ARRAY,SURR, and SPOT,HS were mixed to haveapproximately commensurate reverberation.
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Recording and Reproduction Experiment
SPOT ARRAY
STR: 3/2 Spot microphones,amplitude panning,time delay.
Williams’ MMA mainarray (LCR).
WFR: 12/2 Spot microphones,Sonic Emotion WFSprocessor, delay.
Acoustic curtain.
WFR is rated as more natural than STR.
Notable lack of results in Audio Quality for WFR vs. STR.
Similarly, no difference in coloration between WFR and STR.
WFR is nearly rated as being more stable than STR (statisticaltendency.)
Arrays are rated as more distant than spot based approaches.
Spot based approaches are more localizable and less colored thanarray based approaches.
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Height and Frontal Results
stimulus intensity dB
0.5 100 2 4 8 16
pro
port
ion c
orr
ect
0.4
0.5
0.6
0.7
0.8
0.9
1Height Condition
stimulus intensity dB
0.5 100 2 4 8 16
pro
port
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ect
0.4
0.5
0.6
0.7
0.8
0.9
1Frontal Condition
Averaged over all participants, the threshold is
≈ 6.8 dB for the height condition.≈ 3.2 dB for the frontal condition.
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HATS Measurements of Non-Ideal Diffuse Fields
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Tunings used in Perceptual Experiment
Frequency in Hz10
210
310
4
Γ(m
)
1
1.2
1.4
1.6
1.8
2Normalized Frequency Autocorrelation
Frequency in Hz10
210
310
4
RT
60
in
Se
co
nd
s
0
0.5
1
1.5
2
2.5
3
3.5
4RT60 corresponding to Γ (m)
Frequency in Hz10
210
310
4
Γ(m
)
1
1.2
1.4
1.6
1.8
2Normalized Frequency Autocorrelation
Frequency in Hz10
210
310
4
RT
60
in
Se
co
nd
s
0
0.5
1
1.5
2
2.5
3
3.5
4RT60 corresponding to Γ (m)
Left - Γ(m) describing the frequency autocorrelation measured from theensemble of I decorrelation filters.
Right - correspondence between Γ(m) and physical RT60 values.
Tunings for RIR of Redpath Hall (top) and Tanna Hall (bottom) at McGillUniversity.
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Simulated Fields - Method
Use cardioid K/H approximation:
p(r) =−jkm
2
√−2π
jkm
I∑i=1
E(i)
[G(ri ) +
∇G(ri ) · ni
jkm
]∆s (9)
WFS “stationary phase approximation” →√
−2πjkm
Reference case: measured edge values from simulated field.
Matrix’d cardioid from B-Format:
C(i) = W + cos(φi )X + sin(φi )Y
Dud case: No decorrelation E (i) = C (i)
DFM decorrelation filters E (i) = Di (m)C (i)
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Definition of Γ(m) and Relationship to RT60
Γ(m) is the frequency autocorrelation of the ensemble of filters:
Γ(m) =
∑I |Di (m)|2|Di (m + 1)|2
(∑
I |Di (m)|2)2(10)
Manipulate the relationship between RT60 and FAC:
RT60 = 6.9τ =6.9
∆ω
√√√√[ 〈p2(ω)p2(ω + ∆ω)〉〈p2(ω)〉2
− 1
]−1
− 1 (11)
Substituting Γ(m) from the ensemble of filters yields:
RT60(m)est = 6.9τ =6.9
ωbin
√[Γ(m)− 1]−1 − 1 (12)
Discrete frequency Ωm → continuous frequency ω.
Bin spacing ωbin = Fs/M → continuous frequency offset ∆ω.
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Experiment 1: Setup
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Experiment 1 Format
Treatment DescriptionReflections modeled Direct sound clipped off and the RIR faded in af-
ter the early reflections. Five first early reflectionsmodeled based on room geometry.
Reflections off Direct sound clipped off and the RIR faded in afterthe early reflections.
Clipped Direct sound clipped off from the RIR. Early reflec-tions left intact.
Clipped scrambled Direct sound clipped off from the RIR. Orientationof the virtual cardioids scrambled.
Clipped 0th Direct sound clipped off from the RIR. Only omni-channel used for deriving loudspeaker signals.
Session 1: free text response and rating scale realism.
Session 2: rating scales: timbral quality and spatial quality.
J.S. Bach Allemande (Suite No. 3 in C) (cello.)
Broken Crank (drums and electric bass.)
Tanna and Redpath Halls at McGill University.
Fully factorial ANOVA, p = 0.05.
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Experiment 1: Rating Results
******
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lect
ions
mod
eled
Clip
ped
Ref
lect
ions of
f
Clip
ped
scra
mbl
ed
Clip
ped
0th
Rendering
Rea
lism
*****
0
25
50
75
100
Ref
lect
ions
mod
eled
Clip
ped
Ref
lect
ions of
f
Clip
ped
scra
mbl
ed
Clip
ped
0th
Rendering
Tim
bral
qua
lity
******
0
25
50
75
100
Ref
lect
ions
mod
eled
Clip
ped
Ref
lect
ions of
f
Clip
ped
scra
mbl
ed
Clip
ped
0th
Rendering
Spa
tial q
ualit
y
Post-experiment questionnaire: 11 of 16 sound recording professionals foundDFM usable.
Complexity of geometric reflections is justified.
*** → p = 0; ** → p < 0.01; * → p < 0.05.
51 / 57
Romblom
Overview
Non-IdealReverberation
DFM: Algorithm
Contributions
Extra Stuff
Constrained Correspondence Analysis (CCA)
−2 −1 0 1 2
−1
0
1
2
3
Constrained Correspondence Axis 1: 36 %
Con
stra
ined
Cor
resp
onde
nce
Axi
s 2:
28
%
Artificial
Being there
Comb filtering
Good Balance DR
Natural
Not enough reverberation
Phase problems
Poor balance DR
Poor early reflections
Poor sense of space
Realistic
Sense of space
Smaller than expected
Timbre problems
Too distant
Too reverberant
Unnatural
Unnatural reverberation
UnrealisticClipped
Clippedscrambled
Clipped 0th
Reflectionsmodeled
Reflectionsoff
52 / 57
Romblom
Overview
Non-IdealReverberation
DFM: Algorithm
Contributions
Extra Stuff
Experiment 2: Objective and Format
Is DFM comparable to the state of the art?
For 5.1, compare:
DFM using geometrically modeled reflections.Hamasaki Square.Outward-Facing Cardioids (OFC.)
Format:
Rating scales: Overall Quality, Depth of Stage, and Sense ofSpace.Faure Sicilienne (cello) and progressive rock band.Tanna and Redpath halls.CIRMMT Critical Listening Lab.Magnitude rating analysis identical to experiment 1.
53 / 57
Romblom
Overview
Non-IdealReverberation
DFM: Algorithm
Contributions
Extra Stuff
Experiment 2: Setup
54 / 57
Romblom
Overview
Non-IdealReverberation
DFM: Algorithm
Contributions
Extra Stuff
Experiment 2: User Interface
55 / 57
Romblom
Overview
Non-IdealReverberation
DFM: Algorithm
Contributions
Extra Stuff
Experiment 2: Rating Scales
0
25
50
75
100
DFM HS O
FC
Method
Ove
rall q
uality
**
0
25
50
75
100
DFM HS O
FC
Method
Dept
h of
sta
ge
0
25
50
75
100
DFM HS O
FC
Method
Sens
e of
spa
ce
56 / 57
Romblom
Overview
Non-IdealReverberation
DFM: Algorithm
Contributions
Extra Stuff
Experiment 2: Source Positions
DFM HS OFC
+ + +
ObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserver
+ + +
ObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserver
+ + +
ObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserver
+ + +
ObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserver
++ +
ObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserver
++ +
ObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserver
0
100
200
300
400
0
100
200
300
400
Large hallM
edium hall
−200 −100 0 100 200−200 −100 0 100 200−200 −100 0 100 200Width [px]
Dis
tanc
e [p
x]
Bass Drums Synthesizer
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