Reduced-bandwidth and distributed MWF-based noise reduction algorithms Simon Doclo, Tim Van den...

Reduced-bandwidth and Reduced-bandwidth and

distributed MWF-based noise distributed MWF-based noise

reduction algorithmsreduction algorithms

Simon Doclo, Tim Van den Bogaert, Jan Wouters, Marc Moonen

Dept. of Electrical Engineering (ESAT-SCD), KU Leuven, Belgium Laboratory for Exp. ORL, KU Leuven, Belgium

WASPAA-2007, Oct 23 2007

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OutlineOutline

• Hearing aids: bilateral vs. binaural processing

• Binaural multi-channel Wiener filter: transmit all microphone signals large bandwidth of wireless link

• Reduce bandwidth: transmit only one contralateral signal

o signal-independent: contralateral microphone, fixed beamformer

o signal-dependent: MWF on contralateral microphones

o iterative distributed MWF procedure:

– rank-1 speech correlation matrix converges to B-MWF solution !

– can still be used in practice when assumption is not satisfied

• Performance comparison:

o SNR improvement (+ spatial directivity pattern)

o dB-MWF performance approaches quite well binaural MWF performance for all conditions

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• Many hearing impaired are fitted with hearing aid at both ears:o Signal processing to reduce background noise and improve

speech intelligibility

o Signal processing to preserve directional hearing (ILD/ITD cues)

o Multiple microphone available: spectral + spatial processing

IPD/ITD

ILD

Hearing aids: bilateral vs. binauralHearing aids: bilateral vs. binaural

Bilateral/binaural Binaural MWF

Bandwidth reduction

Experimental results

Conclusions

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Bilateral system

Independent left/right processing:binaural cues for localisation aredistorted

Binaural system

- Larger SNR improvement (more microphones) - Preservation of binaural cues possible

Need for binaural link


Bandwidth reduction


Conclusions

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• Binaural multi-microphone noise reduction techniques:

o Fixed beamforming

– Low complexity, but limited performance

o Adaptive beamforming

– Mostly based on GSC structure + e.g. passing low-pass portion unaltered to preserve ITD cues

o Computational auditory scene analysis

– Computation of (real-valued) binaural mask based on binaural and temporal/spectral cues

o Multi-channel Wiener filtering

– MMSE-based estimate of speech component in both hearing aids

– Extensions for preserving binaural cues of speech and noise components

[Desloge 1997, Merks 1997, Lotter 2006]

[Welker 1997, Nishimura 2002, Lockwood 2004]

[Kollmeier 1993, Wittkop

2003, Hamacher 2002, Haykin

2004]

[Doclo, Klasen, Van den Bogaert, Wouters, Moonen 2005-2007]


Bandwidth reduction


Conclusions

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Configuration and notationConfiguration and notation• M microphones on each hearing aid: Y0 , Y1

• Speech and noise components:

• Single speech source: (acoustic transfer functions)

• Collaboration: 2N signals transmitted between hearing aids


Bandwidth reduction


Conclusions

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Binaural MWF (B-MWF)Binaural MWF (B-MWF)

• SDW-MWF using all 2M microphones from both hearing aids:

o All microphone signals are transmitted:

o MMSE estimate of speech component in (front) microphone ofleft and right hearing aid + trade-off ()

noise reduction

speech distortionspeech componentin front microphone

• Binaural MWF cost function:

Estimated during speech-and-noise and noise-only periods: VAD


Bandwidth reduction


Conclusions

88

Binaural MWF (B-MWF)Binaural MWF (B-MWF)

• Optimal filters (general case):

• Optimal filters (single speech source):

o is complex conjugate of speech ITF

o Optimal filters at left and right hearing aid are parallel


Bandwidth reduction


Conclusions

99

• To limit power/bandwidth requirements, transmit N=1

signal from contralateral hearing aid

o B-MWF can still be obtained, namely if F01 is parallel to and F10 is parallel to infeasible at first sight since full correlation matrices can not be computed !

Reduced-bandwidth algorithmsReduced-bandwidth algorithms


Bandwidth reduction

-fixed beamformer -contralateral MWF -distributed scheme


Conclusions

1010

Fixed beamformerFixed beamformer

• Filters F01 and F10 , which can be viewed as monaural beamformers, are signal-independent

• MWF-front: front contralateral microphone signals

• MWF-superd: monaural superdirective beamformer

limited performance

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Bandwidth reduction



Conclusions

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Contralateral MWFContralateral MWF

• Transmitted signals = output of monaural MWF, estimating the contralateral speech component only using the contralateral microphone signals

o Signal-dependent (better performance than signal-independent)

o Increased computational complexity (two MWF solutions for each hearing aid)

• In general suboptimal solution:

o Optimal solution is obtained in case of single speech source and when noise components between left and right hearing aid are uncorrelated (unrealistic)


Bandwidth reduction



Conclusions

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Distributed MWF (dB-MWF)Distributed MWF (dB-MWF)

• Iterative procedure:

o In each iteration F10 is equal to W00 from previous iteration, and F01 is equal to W11 from previous iteration


Bandwidth reduction



Conclusions

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Bandwidth reduction



Conclusions

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• Single speech source: convergence to B-MWF solution (!)

o MWF cost function decreases in each step of iteration

o Convergence to B-MWF solution, since it minimises J(W) AND

satisfies with

• General case where Rx is not a rank-1 matrix:

o MWF cost function does not necessarily decrease in each iteration

o usually no convergence to optimal B-MWF solution

o Although , dB-MWF procedure

can be used in practice and approaches B-MWF performance


Bandwidth reduction



Conclusions

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Experimental resultsExperimental results• Setup:

o Binaural system with 2 omni microphones on each hearing aid, mounted on CORTEX MK2 artifical head in reverberant room


Bandwidth reduction


-SNR improvement -directivity pattern

Conclusions

o HRTFs: T60 500 ms (and T60 140 ms), fs = 20.48kHz

o Configurations:

– speech source at 0 and several noise configurations (single, two and four noise sources)

– speech source at 90 and noise source at 180

o speech material = HINT, noise material = Auditec babble noise

o Input SNR defined on LF microphone = 0dB (broadband)

o Intelligibility-weighted SNR improvement between output signal and front microphone (L+R)

• MWF processing: o Frequency-domain batch procedure

o L = 128, =5

o Perfect VAD,

o dB-MWF procedure: K=10,

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SNR improvement (500 ms - left HA)SNR improvement (500 ms - left HA)

60 90 120 180 270 300 -60 60 -120 120 120 210 60 120 180 210 60 120 180 270 S90N180 3

4

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Performance comparison (left, L=128, T60

=500 ms)

Angle of noise source(s) (°)

AI

wei

ghte

d S

NR

impr

ovem

ent

(dB

)

B-MWFMWF-frontdB-MWF

Original signal

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• B-MWF:o In general largest SNR improvement of all algorithms

o Up to 4 dB better than MWF-front (3 vs. 4 microphones)

• MWF-superd:o Performance between MWF-front and B-MWF, but in general

worse than (signal-dependent) MWF-contra and dB-MWF

o relatively better performance when (signal-independent) directivity pattern of superdirective beamformer approaches optimal (signal-dependent) directivity pattern of B-MWF, e.g. v=300 (left HA)

• MWF-contra:o Performance between MWF-front and B-MWF

• dB-MWF:o Best performance of all reduced-bandwidth algorithms

o Substantial performance benefit compared to MWF-contra, especially for multiple noise sources

o Performance of dB-MWF approaches quite well performance of

B-MWF, even though speech correlation matrices are not rank-1 due to FFT overlap and estimation errors, i.e.

Experimental resultsExperimental results


Bandwidth reduction



Conclusions

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Experimental resultsExperimental results

• Directivity pattern:

o Fullband spatial directivity pattern of F01, i.e. the pattern generated using the right microphone signals and transmitted to the left hearing aid

o Configuration v=[-120 120], T60 = 140 ms

o B-MWF: null steered towards direction of noise sources optimally signal with high SNR should be transmitted

o MWF-front, MWF-superd: directivity pattern not similar toB-MWF directivity pattern low SNR improvement

o MWF-contra: directivity pattern similar to B-MWF directivity pattern high SNR improvement

o dB-MWF: best performance since directivity pattern closely matches B-MWF directivity pattern

• Using these spatial directivity patterns, it is possible to explain the performance of the different algorithms for different noise configurations to some extent


Bandwidth reduction



Conclusions

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Contralateral directivity patterns (140 Contralateral directivity patterns (140 ms)ms)

B-MWF MWF-front MWF-superd

MWF-contra dB-MWF

v=[-120 120]

2020

ConclusionsConclusions

• Binaural MWF: large bandwidth/power requirement

• Reduced-bandwidth algorithms:o MWF-front, MWF-superd: signal-independent

o MWF-contra: monaural MWF using contralateral microphones

– Signal-dependent, but suboptimal

o dB-MWF: iterative procedure– Converges to B-MWF solution for rank-1 speech correlation

matrix

– Also useful in practice when this assumption is not satisfied

• Experimental results:o dB-MWF > MWF-contra > MWF-superd > MWF-front

– Signal-dependent better than signal-independent

– 2 or 3 iterations sufficient for dB-MWF procedure

– dB-MWF performance approaches quite well B-MWF performance

• Extension: distributed processing in acoustic sensor networks


Bandwidth reduction


Conclusions

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