Reduced-bandwidth and Reduced-bandwidth and distributed MWF-based noise distributed MWF-based noise reduction algorithmsreduction algorithms

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Reduced-bandwidth and Reduced-bandwidth and

distributed MWF-based noise distributed MWF-based noise

reduction algorithms reduction 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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Outline Outline

• 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. binaural Hearing aids: bilateral vs. binaural

Bilateral/binaural

Binaural MWF

Bandwidth reduction

Experimental results

Conclusions

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

Bilateral system

Independent left/right processing:

binaural cues for localisation are

Binaural system

- Larger SNR improvement (more microphones)

Binaural MWF

Conclusions

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

• 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 Computationalauditorysceneanalysis – 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]

[Kollmeier1993,Wittkop 2003, Hamacher2002,Haykin2004]

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

Binaural MWF

Conclusions

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Configuration and notation Configuration 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

Binaural MWF

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 of left and right hearing aid + trade-off ()

noise reduction speech distortion

speech component in front microphone

• Binaural MWF cost function:

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

Binaural MWF

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

Binaural MWF

Conclusions

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• To limit power/bandwidth requirements, transmitN=1signal 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 algorithms Reduced-bandwidth algorithms

Binaural MWF

Bandwidth reduction -fixed beamformer -contralateral MWF -distributed scheme

Conclusions

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Fixed beamformer Fixed 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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Binaural MWF

Conclusions

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Contralateral MWF Contralateral 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)

Binaural MWF

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

Binaural MWF

Conclusions

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

Binaural MWF

Conclusions

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

• 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 R_x 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

Binaural MWF

Conclusions

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Experimental results Experimental results

• Setup:

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

Binaural MWF

Experimental results -SNR improvement -directivity pattern

Conclusions

o HRTFs: T60  500 ms (and T₆₀  140 ms), f_s= 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)

4 5 6 7 8 9 10 11 12 13

Performance comparison (left, L=128, T₆₀=500 ms)

AI weighted SNR improvement (dB)

B-MWF MWF-front dB-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 results Experimental results

Binaural MWF

Conclusions

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Experimental results Experimental 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 to B-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

Binaural MWF

Conclusions

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

B-MWF MWF-front MWF-superd

MWF-contra dB-MWF

_v=[-120 120]

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Conclusions Conclusions

• 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

Binaural MWF

Conclusions