Comparison of RTF Estimation Methods between a Head-Mounted Binaural Hearing Device and an External Microphone

Transcription

1 Comparison of RTF Estimation Methods between a Head-Mounted Binaural Hearing Device and an External Microphone Nico Gößling, Daniel Marquardt and Simon Doclo Department of Medical Physics and Acoustics Signal Processing Group Carl von Ossietzky University of Oldenburg August 19, 2017, Stockholm, Sweden Nico Gößling /15

2 Motivation Nico Gößling Motivation 2/15

3 Motivation Improve speech intelligibility in noisy scenarios diffuse background noise Nico Gößling Motivation 3/15

4 Motivation Improve speech intelligibility in noisy scenarios Preserve binaural cues to assure spatial awareness target speaker diffuse background noise Nico Gößling Motivation 3/15

5 Motivation Improve speech intelligibility in noisy scenarios Preserve binaural cues to assure spatial awareness Adding an external microphone (emic) to head-mounted hearing devices (HHDs) improves algorithm performance target speaker HHDs emic [Szurley2016], [Gößling2017] diffuse background noise Nico Gößling Motivation 3/15

6 Motivation Fixed beamforming is possible for head-mounted microphones Nico Gößling Motivation 4/15

7 Motivation Fixed beamforming is possible for head-mounted microphones Position of emic is unknown Nico Gößling Motivation 4/15

8 Motivation Fixed beamforming is possible for head-mounted microphones Position of emic is unknown Nico Gößling Motivation 4/15

9 Motivation Fixed beamforming is possible for head-mounted microphones Position of emic is unknown Nico Gößling Motivation 4/15

10 Motivation Fixed beamforming is possible for head-mounted microphones Position of emic is unknown Nico Gößling Motivation 4/15

11 Motivation Fixed beamforming is possible for head-mounted microphones Position of emic is unknown Estimate the acoustic transfer functions (ATFs) or... ATF ATF Nico Gößling Motivation 4/15

12 Motivation Fixed beamforming is possible for head-mounted microphones Position of emic is unknown Estimate the acoustic transfer functions (ATFs) or estimate the relative transfer functions (RTFs) between emic and HHDs RTF RTF Nico Gößling Motivation 4/15

13 Binaural Noise Reduction Nico Gößling Binaural Noise Reduction 5/15

14 Extended Binaural Noise Reduction System Y L,1 Y L,2 Y R,1 Y R,2.. Y L,ML Y R,MR left HHD w L w R right HHD Z L Z R Nico Gößling Binaural Noise Reduction 6/15

15 Extended Binaural Noise Reduction System Y e emic Y L,1 Y L,2 Y R,1 Y R,2.. Y L,ML Y R,MR left HHD w L w R right HHD Z L Z R Nico Gößling Binaural Noise Reduction 6/15

16 Extended Binaural Noise Reduction System Y e emic Y L,1 Y L,2 Y R,1 Y R,2.. Y L,ML Y R,MR left HHD w L w R right HHD Z L Z R Input signal vector: y(k, l) = x(k, l) + n(k, l) Single speech source: x(k, l) = a(k, l)s(k, l) Nico Gößling Binaural Noise Reduction 6/15

17 Relative Transfer Functions (RTFs) The relative transfer function (RTF) vectors, relating the ATF vector to the reference microphones, are defined as: h L [ = a A L = 1, A L,2 A L,..., A L,M L h R [ = a AL,1 A R = A R,..., A L,M L A L, A R,1 A L A R, 1, A R,2 A R,..., A R,M R A L,..., A R,M R A R, A e A L ] T, A e A R ] T Nico Gößling Binaural Noise Reduction 7/15

18 Relative Transfer Functions (RTFs) The relative transfer function (RTF) vectors, relating the ATF vector to the reference microphones, are defined as: h L [ = a A L = 1, A L,2 A L,..., A L,M L h R [ = a AL,1 A R = A R,..., A L,M L A L, A R,1 A L A R, 1, A R,2 A R,..., A R,M R A L,..., A R,M R A R, A e A L ] T, A e A R ] T Last entry relates the emic to the (head-mounted) reference microphones and needs to be estimated Nico Gößling Binaural Noise Reduction 7/15

19 Binaural MVDR Beamforming Minimizing output noise power Preserving speech component in reference microphone signals Optimization problem for left filter { w w L = arg min n w s.t. w H x = X L Solution for left filter w L = R 1 n a a H R 1 n a A L = R 1 n h L h H L R 1 n h L Nico Gößling Binaural Noise Reduction 8/15

20 Binaural MVDR Beamforming Minimizing output noise power Preserving speech component in reference microphone signals Optimization problem for left filter { w w L = arg min n w s.t. w H x = X L Solution for left filter w L = R 1 n a a H R 1 n a A L = R 1 n h L h H L R 1 n h L ATFs are not required to steer the beamformer! Nico Gößling Binaural Noise Reduction 8/15

21 RTF estimation methods Nico Gößling RTF estimation methods 9/15

22 RTF vector construction Fixed beamforming for head-mounted microphones, e.g, based on direction-of-arrival θ (DOA) estimation RTFs can be measured or simulated in advance Not possible for emic RTF estimation is needed DOA est. h L (θ), h R (θ) y h L, h R RTF est. H e,l, H e,r Nico Gößling RTF estimation methods 10/15

23 RTF estimation methods Ground truth: H e,l = et e R x e L e T L R xe L = A e A L Biased approach: H b e,l = et e Ry e L e T L Ry e L MVDR pre-processing approach: H pp e,l = et e Ry w H,L w H H,L Ry w H,L Covariance whitening approach: H cw e,l = et e R1/2 n v max e T L R1/2 n v max Nico Gößling RTF estimation methods 11/15

24 Experimental results Nico Gößling Experimental results 12/15

25 Experimental setup 7 m 6 m 35 2 m 0.5 m Male target speaker Diffuse babble noise M L = M R = 2 T60 = 350 ms ms (50% OL) Assuming θ = 35 R n estimation during 2 s noise-only initialization R y estimation during 18 s speech-plus-noise Batch filtering Nico Gößling Experimental results 13/15

26 Experimental results Nico Gößling Experimental results 14/15

27 Experimental results Nico Gößling Experimental results 14/15

28 Experimental results Nico Gößling Experimental results 14/15

29 Experimental results Nico Gößling Experimental results 14/15

30 Conclusion: Conclusion and Outlook 1 It is possible to estimate RTFs and incorporate an emic into a binaural MVDR beamformer in an experimental scenario based on real-world signals 2 Pre-processing proved beneficial compared to baised approach 3 Covariance whitening outperformed the other approaches Outlook: 1 Directional noise sources (extended BLCMV beamformer) 2 Practical problems, e.g. synchronization (influence of clock drift/offset), online implementations and dynamic scenarios Nico Gößling Experimental results 15/15

31 Conclusion: Conclusion and Outlook 1 It is possible to estimate RTFs and incorporate an emic into a binaural MVDR beamformer in an experimental scenario based on real-world signals 2 Pre-processing proved beneficial compared to baised approach 3 Covariance whitening outperformed the other approaches Outlook: 1 Directional noise sources (extended BLCMV beamformer) 2 Practical problems, e.g. synchronization (influence of clock drift/offset), online implementations and dynamic scenarios Thanks for your attention! Nico Gößling Experimental results 15/15

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