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Enhanced Noise Reduction for Hearing Aids: Distributed MWF Algorithms

Explore reduced-bandwidth and distributed MWF algorithms for improved hearing aid noise reduction and speech intelligibility. Compare bilateral vs. binaural processing and the benefits of multi-channel Wiener filtering. Experimentally evaluate the performance and preserve binaural cues for optimal results.

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Enhanced Noise Reduction for Hearing Aids: Distributed MWF Algorithms

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  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. 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 • signal-independent: contralateral microphone, fixed beamformer • signal-dependent: MWF on contralateral microphones • 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: • SNR improvement (+ spatial directivity pattern) • dB-MWF performance approaches quite well binaural MWF performance for all conditions

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

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

  5. Hearing aids: bilateral vs. binaural • Bilateral/binaural • Binaural MWF • Bandwidth reduction • Experimental results • Conclusions • Binaural multi-microphone noise reduction techniques: • Fixed beamforming • Low complexity, but limited performance • Adaptive beamforming • Mostly based on GSC structure + e.g. passing low-pass portion unaltered to preserve ITD cues • Computationalauditorysceneanalysis • Computation of (real-valued) binaural mask based on binaural and temporal/spectral cues • 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,Wittkop2003, Hamacher2002,Haykin2004] [Doclo, Klasen, Van den Bogaert, Wouters, Moonen 2005-2007]

  6. Configuration and notation • M microphones on each hearing aid: Y0 , Y1 • Speech and noise components: • Single speech source: (acoustic transfer functions) • Collaboration: 2Nsignals transmitted between hearing aids • Bilateral/binaural • Binaural MWF • Bandwidth reduction • Experimental results • Conclusions

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

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

  9. Reduced-bandwidth algorithms • To limit power/bandwidth requirements, transmitN=1signal from contralateral hearing aid • 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 ! • Bilateral/binaural • Binaural MWF • Bandwidth reduction -fixed beamformer -contralateral MWF -distributed scheme • Experimental results • Conclusions

  10. 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 • Bilateral/binaural • Binaural MWF • Bandwidth reduction -fixed beamformer -contralateral MWF -distributed scheme • Experimental results • Conclusions

  11. Contralateral MWF • Transmitted signals = output of monaural MWF, estimating the contralateral speech component only using the contralateral microphone signals • Signal-dependent (better performance than signal-independent) • Increased computational complexity (two MWF solutions for each hearing aid) • In general suboptimal solution: • Optimal 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. Distributed MWF (dB-MWF) • Iterative procedure: • In each iteration F10 is equal to W00 from previous iteration, and F01 is equal to W11 from previous iteration • Bilateral/binaural • Binaural MWF • Bandwidth reduction -fixed beamformer -contralateral MWF -distributed scheme • Experimental results • Conclusions

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

  14. Distributed MWF (dB-MWF) • Single speech source: convergence to B-MWF solution (!) • MWF cost function decreases in each step of iteration • Convergence to B-MWF solution, since it minimises J(W) AND satisfies with • General case where Rx is not a rank-1 matrix: • MWF cost function does not necessarily decrease in each iteration • usually no convergence to optimal B-MWF solution • Although , 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. HRTFs: T60 500 ms (and T60 140 ms), fs = 20.48kHz • Configurations: • speech source at 0 and several noise configurations (single, two and four noise sources) • speech source at 90 and noise source at 180 • speech material = HINT, noise material = Auditec babble noise • Input SNR defined on LF microphone = 0dB (broadband) • Intelligibility-weighted SNR improvement between output signal and front microphone (L+R) • MWF processing: • Frequency-domain batch procedure • L = 128, =5 • Perfect VAD, • dB-MWF procedure: K=10, Experimental results • Setup: • Binaural 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

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

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

  18. Experimental results • Directivity pattern: • Fullband spatial directivity pattern of F01, i.e. the pattern generated using the right microphone signals and transmitted to the left hearing aid • Configuration v=[-120 120], T60 = 140 ms • B-MWF: null steered towards direction of noise sources  optimally signal with high SNR should be transmitted • MWF-front, MWF-superd: directivity pattern not similar toB-MWF directivity pattern  low SNR improvement • MWF-contra: directivity pattern similar to B-MWF directivity pattern  high SNR improvement • 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 • Bilateral/binaural • Binaural MWF • Bandwidth reduction • Experimental results -SNR improvement -directivity pattern • Conclusions

  19. Contralateral directivity patterns (140 ms) B-MWF MWF-front MWF-superd MWF-contra dB-MWF v=[-120 120]

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

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