DFM Overview

Romblom

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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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Overview

1 Overview

2 Non-Ideal Reverberation

3 DFM: Algorithm

4 Contributions

5 Extra Stuff

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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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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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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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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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Ratings for the Distance Scale (Near/Far)

SPOT,DRY ARRAY,DRY ARRAY,SURR SPOT,HS ARRAY,HS0

10

20

30

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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

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80

90

100

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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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Non-Ideal Diffuse Field Reverberation

1 Overview


3 DFM: Algorithm

4 Contributions

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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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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

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2

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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

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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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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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Diffuse Field Modeling Algorithm

1 Overview


3 DFM: Algorithm

4 Contributions

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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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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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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

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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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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

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-0.6

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1200 Hz, time constant 100 ms

Frequency50 100 150 200 250 300 350 400 450 500

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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

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5

10

15

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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

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1Autocorrelation 40 Hz

meters

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C2CNoneDFM

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meters

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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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Contributions

1 Overview


3 DFM: Algorithm

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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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Extra Stuff

1 Overview


3 DFM: Algorithm

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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

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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

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RT

60

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4RT60 corresponding to Γ (m)

Frequency in Hz10

210

310

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Γ(m

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2Normalized Frequency Autocorrelation

Frequency in Hz10

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RT

60

in

Se

co

nd

s

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0.5

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2.5

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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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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.

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Constrained Correspondence Analysis (CCA)

−2 −1 0 1 2

−1

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Constrained Correspondence Axis 1: 36 %

Con

stra

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Cor

resp

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Axi

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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

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DFM: Algorithm

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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.

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Overview


DFM: Algorithm

Contributions

Extra Stuff

Experiment 2: Setup

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Overview


DFM: Algorithm

Contributions

Extra Stuff

Experiment 2: User Interface

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Overview


DFM: Algorithm

Contributions

Extra Stuff

Experiment 2: Rating Scales

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DFM HS O

FC

Method

Ove

rall q

uality

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h of

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ge

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e of

spa

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Overview


DFM: Algorithm

Contributions

Extra Stuff

Experiment 2: Source Positions

DFM HS OFC

+ + +

ObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserver

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ObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserverObserver

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++ +


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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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DFM Overview

Engineering

Transcript of DFM Overview