Reduced-bandwidth and distributed MWF-based noise reduction algorithms Simon Doclo, Tim Van den Bogaert, Jan Wouters, Marc Moonen Dept. of Electrical Engineering.

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

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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

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• 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

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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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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

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Performance comparison (left, L=128, T60

=500 ms)

Angle of noise source(s) (°)

AI

wei

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d S

NR

impr

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(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

Reduced-bandwidth and distributed MWF-based noise reduction algorithms Simon Doclo, Tim Van den Bogaert, Jan Wouters, Marc Moonen Dept. of Electrical Engineering.

Documents

binaural mwf performance

binaural cues of speech

contralateral hearing

binaural mask

hearing aidsextensions

bmwf solution

microphone of left

outlinehearing aids