1. Reduced-bandwidth and distributed MWF-based noise 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

2. 2 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 osignal-independent: contralateral microphone, fixed beamformer osignal-dependent: MWF on contralateral microphones oiterative 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: oSNR improvement (+ spatial directivity pattern) odB-MWF performance approaches quite well binaural MWF performance for all conditions

3. 3 Many hearing impaired are fitted with hearing aid at both ears: oSignal processing to reduce background noise and improve speech intelligibility oSignal processing to preserve directional hearing (ILD/ITD cues) oMultiple microphone available: spectral + spatial processing IPD/ITD ILD Hearing aids: bilateral vs. binaural  Bilateral/binaural  Binaural MWF  Bandwidth reduction  Experimental results  Conclusions

4. 4 Hearing aids: bilateral vs. binaural Bilateral system Independent left/right processing: binaural cues for localisation are distorted Binaural system - Larger SNR improvement (more microphones) - Preservation of binaural cues possible Need for binaural link  Bilateral/binaural  Binaural MWF  Bandwidth reduction  Experimental results  Conclusions

5. 5 Hearing aids: bilateral vs. binaural Binaural multi-microphone noise reduction techniques: oFixed beamforming – Low complexity, but limited performance oAdaptive beamforming – Mostly based on GSC structure + e.g. passing low-pass portion unaltered to preserve ITD cues oComputational auditory scene analysis – Computation of (real-valued) binaural mask based on binaural and temporal/spectral cues oMulti-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]  Bilateral/binaural  Binaural MWF  Bandwidth reduction  Experimental results  Conclusions

6. 6 Configuration and notation M microphones on each hearing aid: Y 0, Y 1 Speech and noise components: Single speech source: (acoustic transfer functions) Collaboration: 2N signals transmitted between hearing aids  Bilateral/binaural  Binaural MWF  Bandwidth reduction  Experimental results  Conclusions

7. 7 Binaural MWF (B-MWF) SDW-MWF using all 2M microphones from both hearing aids: oAll microphone signals are transmitted: oMMSE estimate of speech component in (front) microphone of left and right hearing aid + trade-off (  ) noise reductionspeech distortion speech component in front microphone Binaural MWF cost function: Estimated during speech-and-noise and noise-only periods: VAD  Bilateral/binaural  Binaural MWF  Bandwidth reduction  Experimental results  Conclusions

8. 8 Binaural MWF (B-MWF) Optimal filters (general case): Optimal filters (single speech source): o is complex conjugate of speech ITF oOptimal filters at left and right hearing aid are parallel  Bilateral/binaural  Binaural MWF  Bandwidth reduction  Experimental results  Conclusions

9. 9 To limit power/bandwidth requirements, transmit N=1 signal from contralateral hearing aid oB-MWF can still be obtained, namely if F 01 is parallel to and F 10 is parallel to  infeasible at first sight since full correlation matrices can not be computed ! Reduced-bandwidth algorithms  Bilateral/binaural  Binaural MWF  Bandwidth reduction -fixed beamformer -contralateral MWF -distributed scheme  Experimental results  Conclusions

10. 10 Fixed beamformer Filters F 01 and F 10, which can be viewed as monaural beamformers, are signal-independent MWF-front: front contralateral microphone signals MWF-superd: monaural superdirective beamformer limited performance  Bilateral/binaural  Binaural MWF  Bandwidth reduction -fixed beamformer -contralateral MWF -distributed scheme  Experimental results  Conclusions

11. 11 Contralateral MWF Transmitted signals = output of monaural MWF, estimating the contralateral speech component only using the contralateral microphone signals oSignal-dependent (better performance than signal-independent) oIncreased computational complexity (two MWF solutions for each hearing aid) In general suboptimal solution: oOptimal solution is obtained in case of single speech source and when noise components between left and right hearing aid are uncorrelated (unrealistic)  Bilateral/binaural  Binaural MWF  Bandwidth reduction -fixed beamformer -contralateral MWF -distributed scheme  Experimental results  Conclusions

12. 12 Distributed MWF (dB-MWF) Iterative procedure: oIn each iteration F 10 is equal to W 00 from previous iteration, and F 01 is equal to W 11 from previous iteration  Bilateral/binaural  Binaural MWF  Bandwidth reduction -fixed beamformer -contralateral MWF -distributed scheme  Experimental results  Conclusions

13. 13 Distributed MWF (dB-MWF)  Bilateral/binaural  Binaural MWF  Bandwidth reduction -fixed beamformer -contralateral MWF -distributed scheme  Experimental results  Conclusions

14. 14 Distributed MWF (dB-MWF) Single speech source: convergence to B-MWF solution (!) oMWF cost function decreases in each step of iteration oConvergence to B-MWF solution, since it minimises J(W) AND satisfies with General case where R x is not a rank-1 matrix: oMWF cost function does not necessarily decrease in each iteration ousually no convergence to optimal B-MWF solution oAlthough, dB-MWF procedure can be used in practice and approaches B-MWF performance  Bilateral/binaural  Binaural MWF  Bandwidth reduction -fixed beamformer -contralateral MWF -distributed scheme  Experimental results  Conclusions

15. 15 Experimental results Setup: oBinaural system with 2 omni microphones on each hearing aid, mounted on CORTEX MK2 artifical head in reverberant room  Bilateral/binaural  Binaural MWF  Bandwidth reduction  Experimental results -SNR improvement -directivity pattern  Conclusions oHRTFs: T 60  500 ms (and T 60  140 ms), f s = 20.48kHz oConfigurations: – speech source at 0  and several noise configurations (single, two and four noise sources) – speech source at 90  and noise source at 180  ospeech material = HINT, noise material = Auditec babble noise oInput SNR defined on LF microphone = 0dB (broadband) oIntelligibility-weighted SNR improvement between output signal and front microphone (L+R) MWF processing: oFrequency-domain batch procedure oL = 128,  =5 oPerfect VAD, odB-MWF procedure: K=10,

16. 16 SNR improvement (500 ms - left HA) Original signal

17. 17 B-MWF: oIn general largest SNR improvement of all algorithms oUp to 4 dB better than MWF-front (3 vs. 4 microphones) MWF-superd: oPerformance between MWF-front and B-MWF, but in general worse than (signal-dependent) MWF-contra and dB-MWF orelatively 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: oPerformance between MWF-front and B-MWF dB-MWF: oBest performance of all reduced-bandwidth algorithms oSubstantial performance benefit compared to MWF-contra, especially for multiple noise sources oPerformance 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  Bilateral/binaural  Binaural MWF  Bandwidth reduction  Experimental results -SNR improvement -directivity pattern  Conclusions

18. 18 Experimental results Directivity pattern: oFullband spatial directivity pattern of F 01, i.e. the pattern generated using the right microphone signals and transmitted to the left hearing aid oConfiguration  v =[-120  120  ], T 60 = 140 ms oB-MWF: null steered towards direction of noise sources  optimally signal with high SNR should be transmitted oMWF-front, MWF-superd: directivity pattern not similar to B-MWF directivity pattern  low SNR improvement oMWF-contra: directivity pattern similar to B-MWF directivity pattern  high SNR improvement odB-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  Bilateral/binaural  Binaural MWF  Bandwidth reduction  Experimental results -SNR improvement -directivity pattern  Conclusions

19. 19 Contralateral directivity patterns (140 ms) B-MWFMWF-frontMWF-superd MWF-contradB-MWF  v =[-120  120  ]

20. 20 Conclusions Binaural MWF: large bandwidth/power requirement Reduced-bandwidth algorithms: oMWF-front, MWF-superd: signal-independent oMWF-contra: monaural MWF using contralateral microphones – Signal-dependent, but suboptimal odB-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: odB-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  Bilateral/binaural  Binaural MWF  Bandwidth reduction  Experimental results  Conclusions