audio and speech processing topic 5: acoustic feedback control

Optimization in Audio Signal Processing

Audio and Speech Processing

Topic 5: Acoustic Feedback ControlToon van Waterschoot/Marc Moonen

Dept. E.E./ESAT, KU [email protected]@esat.kuleuven.beOutlineIntroductionAcoustic feedback controlNotch-filter-based howling suppression (NHS)Adaptive feedback cancellation (AFC)Conclusion & open issuesOutlineIntroductionsound reinforcementacoustic feedbackAcoustic feedback controlNotch-filter-based howling suppression (NHS)Adaptive feedback cancellation (AFC)Conclusion & open issues

Introduction (1): Sound reinforcement (1)Goal: to deliver sufficiently high sound level and best possible sound quality to audience

sound sourcesmicrophonesmixer & amploudspeakersmonitorsroomaudienceLinear system model: multi-channel single-channel

We will mostly restrict ourselves to the single-channel (= single-loudspeaker-single-microphone) case

Introduction (2): Sound reinforcement (2)

Introduction (3): Sound reinforcement (3)Assumptions (for now):loudspeaker has linear & flat responsemicrophone has linear & flat responseforward path (amp) has linear & flat responseacoustic feedback path has linear responseBut: acoustic feedback path has non-flat response

Acoustic feedback path response: example room (36 m3) impulse response frequency magnitude responseIntroduction (4): Sound reinforcement (4)

direct couplingearly reflectionsdiffuse sound field

peaks/dips = anti-nodes/nodes of standing wavespeaks ~10 dB above average, and separated by ~10 HzDesired system transfer function:

Closed-loop system transfer function:

spectral colorationacoustic echoesrisk of instabilityLoop response:loop gainloop phase

Introduction (5): Acoustic feedback (1)

Nyquist stability criterion:if there exists a radial frequency for which

then the closed-loop system is unstableif the unstable system is excited at the critical frequency , then an oscillation at this frequency will occur = howlingMaximum stable gain (MSG):maximum forward path gain before instability

2-3 dB gain margin is desirable to avoid ringing


(if G has flat response)

[Schroeder, 1964]Example of closed-loop system instability: loop gain loudspeaker spectrogram


OutlineIntroductionAcoustic feedback controlNotch-filter-based howling suppression (NHS)Adaptive feedback cancellation (AFC)Conclusion & open issuesAcoustic feedback control (1)Goal of acoustic feedback control= to solve the acoustic feedback problemeither completely (to remove acoustic coupling)or partially (to remove howling from loudspeaker signal)Manual acoustic feedback control:proper microphone/loudspeaker selection & positioninga priori room equalization using 1/3 octave graphic EQ filtersad-hoc discrete room modes suppression using notch filtersAutomatic acoustic feedback control:no intervention of sound engineer requireddifferent approaches can be classified into four categoriesAcoustic feedback control (2)phase modulation (PM) methodssmoothing of loop gain (= closed-loop magnitude response)phase/frequency/delay modulation, frequency shiftingwell suited for reverberation enhancement systems (low gain)spatial filtering methods(adaptive) microphone beamforming for reducing direct couplinggain reduction methods(frequency-dependent) gain reduction after howling detectionmost popular method for sound reinforcement applicationsroom modeling methodsadaptive inverse filtering (AIF): adaptive equalization of acoustic feedback path responseadaptive feedback cancellation (AFC): adaptive prediction and subtraction of feedback (howling) component in microphone signalOutlineIntroductionAcoustic feedback controlNotch-filter-based howling suppression (NHS)introductionhowling detectionnotch filter designsimulation resultsAdaptive feedback cancellation (AFC)Conclusion & open issues

Notch-filter-based howling suppression (1): Introductiongain reduction methods:automation of the actions a sound engineer would undertakeclassification of gain reduction methods:automatic gain control (full-band gain reduction)automatic equalization (1/3 octave bandstop filters)NHS: notch-filter-based howling suppression(1/10-1/60 octave filters)NHS subproblems:howling detectionnotch filter design

15Notch-filter-based howling suppression (2): Howling detection (1)howling detection procedure:divide microphone signal in overlapping frames

estimate microphone signal spectrum (DFT)

select number of candidate howling components

calculate set of discriminating signal features

decide on presence/absence of howling

: microphone signal

: set of notch filter design parameterssignal framingfrequency analysispeak pickingfeature calculationhowling detection16Notch-filter-based howling suppression (3): Howling detection (2)discriminating features for howling detection:acoustic feedback example revisited

spectral/temporal features for howling detection

17spectral signal features for howling detection:Peak-to-Threshold Power Ratio (PTPR)Peak-to-Average Power Ratio (PAPR)Peak-to-Harmonic Power Ratio (PHPR)Peak-to-Neighboring Power Ratio (PNPR)temporal signal features for howling detectionInterframe Peak Magnitude Persistence (IPMP)Interframe Magnitude Slope Deviation (IMSD)howling exhibits an exponential amplitude buildup over timehowling components typically persist longer than speech/audiohowling is a non-damped sinusoid, having approx. zero bandwidthhowling does not exhibit a harmonic structure ( in case of clipping!)howling eventually has large power compared to speech/audiohowling should only be suppressed when it is sufficiently loud

Notch-filter-based howling suppression (4): Howling detection (3)18Notch-filter-based howling suppression (5): Howling detection (4)howling detection as a binary hypothesis test:

detection performance:probability of detection

probability of false alarm

example of detection data set:

howling does not occur(Null hypothesis)howling does occur(Alternative hypothesis)

o = positive realizations (NP = 166)x = negative realizations (NN = 482)123456789050010001500200025003000time (s)frequency (Hz)

~ reliability~ sound quality

19Notch-filter-based howling suppression (6): Howling detection (5) example of single-feature howling detection criterion:

evaluation measures:ROC curve: PD vs. PFA PFA for fixed PD = 95 %

criterionPFAPTPR70 %PAPR63 %PHPR37 %PNPR33 %IPMP54 %IMSD40 %00.10.20.30.40.50.60.70.80.9100.10.20.30.40.50.60.70.80.91PFAPDTPAPR= dBTPAPR= 54 dBTPAPR= 52 dBTPAPR= 50 dBTPAPR= 32 dBTPAPR= dB20Notch-filter-based howling suppression (7): Howling detection (6) improved detection with multiple-feature howling detection criteria:logical conjunction of two or more single-feature criteriadesign guideline: combine features with high PD, regardless of PFAexamples of multiple-feature criteria:PHPR & IPMP [Lewis et al. (Sabine Inc.), 1993]FEP = PNPR & IMSD [Osmanovic et al., 2007]PHPR & PNPR, PHPR & IMSD, PNPR & IMSD, PHPR & PNPR & IMSD[van Waterschoot & Moonen, 2008]

single-featurecriterionPFAmultiple-featurecriterionPFAPTPR70 %PHPR & IPMP65 %PAPR63 %FEP24 %PHPR37 %PHPR & PNPR14 %PNPR33 %PHPR & IMSD25 %IPMP54 %PNPR & IMSD5 %IMSD40 %PHPR & PNPR & IMSD3 %21Notch-filter-based howling suppression (8): Notch filter design notch filter design procedure:

set of notch filter design parametersbank of notch filters transfer function

check active filtersnotch filter specificationnotch filter designis a notch filter already active around howling frequency?no? new filter: center frequency = howling frequencyyes? active filter: decrease notch gaintranslate filter specifications into filter coefficients

filter index

22Notch-filter-based howling suppression (9): Simulations results (1) simulation layout:

23Notch-filter-based howling suppression (10): Simulations results (2) simulation results for three different threshold values:

24OutlineIntroductionAcoustic feedback controlNotch-filter-based howling suppression (NHS)Adaptive feedback cancellation (AFC)introductionclosed-loop signal decorrelationadaptive filter designsimulation resultsConclusion & open issuesAdaptive feedback cancellation (1): Introduction (1)AFC concept:predict and subtract entire feedback signal component (howling component!) in microphone signalrequires adaptive estimation of acoustic feedback path modelsimilar to acoustic echo cancellation, but much more difficult due to closed signal loop

Adaptive feedback cancellation (3):Closed-loop signal decorrelation (1)AFC correlation problem:LS estimation bias vector

non-zero bias results in (partial) source signal cancellationLS estimation covariance matrix

with source signal covariance matrix

large covariance results in slow adaptive filter convergencedecorrelation of loudspeaker and source signal is crucial issue!

Adaptive feedback cancellation (4):Closed-loop signal decorrelation (2)Decorrelation in the closed signal loop:noise injectiontime-varying processingnonlinear processingforward path delayInherent trade-off between decorrelation and sound quality

Adaptive feedback cancellation (5):Closed-loop signal decorrelation (3)Decorrelation in the adaptive filtering circuit:adaptive filter delaydecorrelating prefilters

based on source signal model

Sound quality not compromisedAdditional information required:acoustic feedback path delaysource signal model

Adaptive feedback cancellation (6):Adaptive filter designLS-based adaptive filtering algorithms:recursive least squares (RLS)affine projection algorithm (APA)(normalized) least mean squares ((N)LMS)frequency-domain NLMSpartitioned-block frequency domain NLMSprediction-error-method(PEM)-based adaptive filtering algorithms:joint estimation of acoustic feedback path and source signal modelrequires forward path delay + exploits source signal nonstationarityavailable in all flavours (RLS, APA, NLMS, frequency domain, )25-50 % computational overhead compared to LS-based algorithmsAdaptive feedback cancellation (7):Simulation results (1)simulation layout (revisited):

Adaptive feedback cancellation (8):Simulation results (2)simulation results for three different decorrelation methods: speech music

OutlineIntroductionAcoustic feedback controlNotch-filter-based howling suppression (NHS)Adaptive feedback cancellation (AFC)Conclusion & open issuesConclusion (1):Acoustic feedback control methodsphase modulation methods: suited for low-gain applications such as reverberation enhancementspatial filtering methods:removal of direct coupling if multiple microphones are availablegain reduction methods: notch-filter-based howling suppressionvery popular for sound reinforcement applicationsaccurate howling detection is crucial for sound quality and reliabilityreasonable MSG increase (up to 5 dB) can be attainedroom modeling methods: adaptive feedback cancellationupcoming method as computational resources become cheaperdecorrelation in adaptive filtering circuit for high sound qualityMSG increase up to 20 dB is generally achievedConclusion (1):Open issuesmulti-channel systems:acoustic feedback problem not uniquely defined in multi-channel casemost methods were developed for single-channel case onlycomputational complexity may explodeadaptive feedback cancellation:computational complexity and adaptive filter convergence speed remain problematic due to very high filter orders (~1000 coefficients)adaptive filter behavior in case of undermodeling not well understoodFIR model is inefficient for modeling acoustic resonanceshybrid methods: how to combine different methods such that desirable features are retained while undesirable properties are avoided?interplay between different methods not well understoodand again: computational complexityAdditional literaturereview paper:T. van Waterschoot and M. Moonen, Fifty years of acoustic feedback control: state of the art and future challenges, Proc. IEEE, vol. 99, no. 2, Feb. 2011, pp. 288-327.phase modulation:J. L. Nielsen and U. P. Svensson, Performance of some linear time-varying systems in control of acoustic feedback, J. Acoust. Soc. Amer., vol. 106, no. 1, pp. 240254, Jul. 1999.spatial filtering:G. Rombouts, A. Spriet, and M. Moonen, Generalized sidelobe canceller based combined acoustic feedback- and noise cancellation, Signal Process., vol. 88, no. 3, pp. 571581, Mar. 2008.notch-filter-based howling suppression:T. van Waterschoot and M. Moonen, Comparative evaluation of howling detection criteria in notch-filter-based howling suppression, J. Audio Eng. Soc., Nov. 2010, vol. 58, no. 11, Nov. 2010, pp. 923-940.T. van Waterschoot and M. Moonen, A pole-zero placement technique for designing second-order IIR parametric equalizer filters, IEEE Trans. Audio Speech Lang. Process., vol. 15, no. 8, pp. 25612565, Nov. 2007.adaptive feedback cancellation:G. Rombouts, T. van Waterschoot, K. Struyve, and M. Moonen, Acoustic feedback suppression for long acoustic paths using a nonstationary source model, IEEE Trans. Signal Process., vol. 54, no. 9, pp. 34263434, Sep.2006.G. Rombouts, T. van Waterschoot, and M. Moonen, Robust and efficient implementation of the PEM-AFROW algorithm for acoustic feedback cancellation, J. Audio Eng. Soc., vol. 55, no. 11, pp. 955966, Nov. 2007.T. van Waterschoot and M. Moonen, Adaptive feedback cancellation for audio applications, Signal Process., vol. 89, no. 11, pp. 21852201, Nov. 2009.Questions?

audio and speech processing topic 5: acoustic feedback control

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