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Noise reduction and binaural cue preservation of multi-microphone algorithms

Noise reduction and binaural cue preservation of multi-microphone algorithms. Simon Doclo , Tim van den Bogaert, Marc Moonen, Jan Wouters Dept. of Electrical Engineering (ESAT-SCD), KU Leuven, Belgium Dept. of Neurosciences (ExpORL), KU Leuven, Belgium Oldenburg, June 28 2007. Overview.

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Noise reduction and binaural cue preservation of multi-microphone algorithms

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  1. Noise reduction and binaural cue preservation of multi-microphone algorithms Simon Doclo, Tim van den Bogaert, Marc Moonen, Jan Wouters Dept. of Electrical Engineering (ESAT-SCD), KU Leuven, Belgium Dept. of Neurosciences (ExpORL), KU Leuven, Belgium Oldenburg, June 28 2007

  2. Overview • Problem statement • Improve speech intelligibility + preserve spatial awareness • Bilateral vs. binaural processing • Binaural signal processing using multi-channel Wiener filter • MWF: noise reduction and preservation of speech cues, noise cues are distorted • Extension of MWF to preserve binaural cues of all components: • MWFv: partial estimation of noise component • MWF-ITF: extension with Interaural Transfer Function • Physical and perceptual evaluation • Reduce bandwidth requirements of wireless link • Distributed binaural MWF

  3. Binaural auditory cues: • Interaural Time Difference (ITD) – Interaural Level Difference (ILD) • Binaural cues, in addition to spectral and temporal cues, play an important role in binaural noise reduction and sound localisation • ITD: f < 1500Hz, ILD: f > 2000Hz ILD IPD/ITD Problem statement • Many hearing impaired are fitted with a hearing aid at both ears • Signal processing to selectively enhance useful speech signal and improve speech intelligibility • Signal processing to preserve directional hearing and spatial awareness • Multiple microphone available: spectral + spatial processing • Problem statement -bilateral/binaural • Binaural processing • Bandwidthreduction • Conclusions

  4. Bilateral system Binaural system Independent left/right processing: Preservation of binaural cues for localisation ? More microphones:  better performance ?  preservation of binaural cues ? Need of binaural link Bilateral vs. Binaural • Problem statement -bilateral/binaural • Binaural processing • Bandwidthreduction • Conclusions

  5. RMS error per loudspeaker when accumulating all responses of the different test conditions (NH = normal hearing, NO = hearing impaired without hearing aids, O = omnidirectional configuration, A = adaptive directional configuration) [Van den Bogaert et al., 2006] Bilateral vs. Binaural • Bilateral system: • Independent processing of left and right hearing aid • Localisation cues are distorted • Problem statement -bilateral/binaural • Binaural processing • Bandwidthreduction • Conclusions  also effect on intelligibility through binaural hearing advantage

  6. Bilateral vs. Binaural • Bilateral system: • Independent processing of left and right hearing aid • Localisation cues are distorted • Binaural system: • Cooperation between left and right hearing aid (e.g. wireless link) • Assumption: all microphone signals are available at the same time • Problem statement -bilateral/binaural • Binaural processing • Bandwidthreduction • Conclusions [Van den Bogaert et al., 2006] • Objectives/requirements for binaural algorithm: • SNR improvement: noise reduction, limit speech distortion • Preservation of binaural cues (speech/noise) to exploit binaural hearing advantage • No assumption about position of speech source and microphones

  7. noise component speech component • Use all microphone signals to compute output signal at both ears Configuration and signals • Configuration: microphone array with M microphones at left and right hearing aid, communication between hearing aids • Problem statement • Binaural processing-MWF-Cue preservation-Physical evaluation-Perceptual eval • Bandwidthreduction • Conclusions

  8. trade-off noise reduction and speech distortion Speech-distortion weighted multi-channel Wiener filter (SDW-MWF) [Doclo 2002, Spriet 2004] binaural cue preservation of speech + noise Extension with ITD-ILD or Interaural Transfer Function (ITF) Partial estimation of noise component (MWFv) [Klasen 2005] [Doclo 2005, Klasen 2006, Van den Bogaert 2007] Overview of cost functions • Problem statement • Binaural processing-MWF-Cue preservation-Physical evaluation-Perceptual eval • Bandwidthreduction • Conclusions Multi-channel Wiener filter (MWF): MMSE estimate of speech component in microphone signal at both ears

  9. speech componentin front microphones speech distortion noise reduction estimate • Depends on second-order statistics of speech and noise • Estimate Ry during speech-dominated time-frequency segments, estimate Rv during noise-dominated segments, requiring robust voice activity detection (VAD) mechanism • No assumptions about positions of microphones and sources • Adaptive (LMS-based) algorithm available [Spriet 2004, Doclo 2007] Binaural multi-channel Wiener filter • Binaural SDW-MWF: estimate of speech component in microphone signal at both ears (usually front microphone) + trade-off between noise reduction and speech distortion • Problem statement • Binaural processing-MWF-Cue preservation-Physical evaluation-Perceptual eval • Bandwidthreduction • Conclusions

  10. Binaural cues (ITD-ILD) : Perfectly preserves binaural cues of speech component Binaural cues of noise component  speech component !! Spatial separation between speech and noise sources SNR Binaural multi-channel Wiener filter • Interpretation for single speech source: • Spectral and spatial filtering operation with (spatial) coherence matrix and P (spectral) power • Equivalent to superdirective beamformer (diffuse noise field) or delay-and-sum beamformer (spatially white noise field) +single-channel WF-based postfilter (spectral subraction) • Problem statement • Binaural processing-MWF-Cue preservation-Physical evaluation-Perceptual eval • Bandwidthreduction • Conclusions

  11. Partial noise estimation (MWFv) • Partial estimation of noise component • Estimate of sum of speech component and scaled noise component • Relationship with SDW-MWF: mix with reference microphone signals reduction of noise reduction performance works for multiple noise sources • Problem statement • Binaural processing-MWF-Cue preservation-Physical evaluation-Perceptual eval • Bandwidthreduction • Conclusions

  12. e.g. ITF preservation speech ITF preservation noise Interaural Wiener filter (MWF-ITF) • Extension of SDW-MWF with binaural cues • Add term related to binaural cues of noise (and speech) component • Possible cues: ITD, ILD, Interaural Transfer Function (ITF) • Problem statement • Binaural processing-MWF-Cue preservation-Physical evaluation-Perceptual eval • Bandwidthreduction • Conclusions • Closed form expression! • large  changes direction of speech  increase weight  • Implicit assumption of single noise source

  13. Simulation setup • Identification of HRTFs: • Binaural recordings on CORTEX MK2 artificial head • 2 omni-directional microphones on each hearing aid (d=1cm) • LS = -90:15:90, 90:30:270, 1m from head • Room reverberation: T60=140 ms (and T60=510 ms) • Problem statement • Binaural processing-MWF-Cue preservation-Physical evaluation-Perceptual eval • Bandwidthreduction • Conclusions

  14. Experimental results • Simulations: • SxNy, SNR = 0 dB on left front microphone (broadband) • fs = 20.48 kHz • MWF algorithmic parameters: • batch procedure, perfect VAD • L=96, =5 • MWFv for different , MWF-ITF for different , • Problem statement • Binaural processing-MWF-Cue preservation-Physical evaluation-Perceptual eval • Bandwidthreduction • Conclusions • Physical evaluation: • Speech = HINT, noise = babble noise • Speech intelligibility: SNR • Localisation: ITD / ILD • Perceptual evaluation: • Preliminary study with NH subjects • Speech intelligibility: SRT • Localisation: localise S and N

  15. importance of i-th frequency for speech intelligibility low-pass filter 1500 Hz • ILD error (speech/noise component)  power ratio • ITD error (speech/noise component)  phase of cross-correlation Physical evaluation • Performance measures: • Intelligibility weighted SNR improvement (left/right) • Problem statement • Binaural processing-MWF-Cue preservation-Physical evaluation-Perceptual eval • Bandwidthreduction • Conclusions

  16. Physical evaluation: MWFv • S0N60

  17. speech G HRTFx Mic L Binaural filter S Headphones noise R HRTFv Perceptual evaluation • Procedure: • headphone experiments, using measured HRTFs • Filters are calculated off-line on VU speech-weighted noise as S and multitalker babble noise as N • All stimuli presented at comfort level, 5 NH subjects (ongoing) • Problem statement • Binaural processing-MWF-Cue preservation-Physical evaluation-Perceptual eval • Bandwidthreduction • Conclusions • Speech intelligibility: • Adaptive procedure to find 50% Speech Reception Threshold (SRT) • Localisation: • S and N components (telephone) are sent separately through filter • Localise S and N in room where HRTFs were measured • Level roving 6 dB, 3 repetitions per condition for each subject

  18. Perceptual evaluation: MWFv • Algorithms: unprocessed, state-of-the art bilateral, MWF, MWFv (=0.2) • Conditions: S0N60, S45N315 and S90N270 (T60=510 ms) • Problem statement • Binaural processing-MWF-Cue preservation-Physical evaluation-Perceptual eval • Bandwidthreduction • Conclusions

  19. Perceptual evaluation: MWFv • With state-of-the-art systems: preservation of binaural cues only within central angle of frontal hemisphere. • Binaural MWF: • preserves localization cues for speech source • preserves localization cues for noise source(s) with small mixing  • Recent SRT experiments (N=2) show no substantial SRT difference between =0 and =0.2 • Ongoing research: • Perceptual evaluation (SRT and localisation) for MWF-ITF • Problem statement • Binaural processing-MWF-Cue preservation-Physical evaluation-Perceptual eval • Bandwidthreduction • Conclusions

  20. Bandwidth constraints • Binaural MWF: • 2M microphone signals are transmitted over wireless link • Reduce bandwidth requirement of wireless link: • Transmit one signal from contralateral ear • Problem statement • Binaural processing • Bandwidthreduction • Conclusions • Front contralateral microphone signal • Output of contralateral fixed (e.g. superdirective) beamformer • Output of MWF using only M contralateral microphone signals • Iterative distributed binaural MWF scheme

  21. Physical evaluation • Problem statement • Binaural processing • Bandwidthreduction • Conclusions Performance of dB-MWF close to full binaural MWF !

  22. B-MWF SNR=14.6dB dB-MWF MWF-front MWF-contra SNR=14.2dB SNR=10.5dB SNR=14.2dB Contralateral directivity pattern T60=140 ms S0N120 Left HA

  23. Conclusions • State-of-the art signal processing in (bilateral) HAs: preservation of binaural cues only within central angle of frontal hemisphere • Binaural MWF: • Substantial noise reduction (MWF 4  3 > 2) • Preservation of binaural speech cues • Distortion of binaural noise cues • No assumptions about positions and microphones  VAD • Compromise between noise reduction and binaural cue preservation can be achieved with extensions of MWF • Mixing with microphone signals • Interaural Transfer Function • Reduction of bandwidth using distributed MWF • Problem statement • Binaural processing • Bandwidthreduction • Conclusions

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