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SHORT-TIME MULTICHANNEL NOISE CORRELATION MATRIX ESTIMATORS FOR ACOUSTIC SIGNALS By: Jonathan Blanchette and Martin Bouchard.

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Presentation on theme: "SHORT-TIME MULTICHANNEL NOISE CORRELATION MATRIX ESTIMATORS FOR ACOUSTIC SIGNALS By: Jonathan Blanchette and Martin Bouchard."— Presentation transcript:

1 SHORT-TIME MULTICHANNEL NOISE CORRELATION MATRIX ESTIMATORS FOR ACOUSTIC SIGNALS By: Jonathan Blanchette and Martin Bouchard

2 Overview ▶ Introduction ▶ Framework ▶ Noise correlation matrix estimators ▶ Performance measure ▶ Conclusion & Outlook

3 Introduction ▶ Speech enhancement or beamforming algorithms require the noise Power Spectral Density (PSD). ▶ Many multichannel noise PSD estimation algorithms require some knowledge on sound sources: Number of sources (Many sources are possibly present, time varying) Directivities (Can be unknown) ▶ Additionally many assume that the noise field is diffuse and homogeneous (The noise field could be inhomogeneous). Motivation

4 Framework Noise correlation matrix models

5 Framework Noise correlation matrix models Geometry dependent part

6 Framework Noise correlation matrix models Geometry dependent part

7 Framework Noise correlation matrix models Geometry dependent part scalar

8 Framework Cont’d Noise correlation matrix models Geometry dependent part scalar

9 Framework Cont’d Noise correlation matrix models

10 Framework Cont’d Noise correlation matrix models Geometry dependent part

11 Framework Cont’d Noise correlation matrix models Geometry dependent part

12 Framework Cont’d Noise correlation matrix models Geometry dependent part

13 Framework Cont’d Noise correlation matrix models Geometry dependent partNoise PSD

14 Framework Cont’d Models equivalence in special cases

15 Noise correlation matrix estimators Noisy signal correlation matrix estimate Noisy signal correlation matrix

16 Noise correlation matrix estimators Cont’d Noisy signal correlation matrix Sources correlation matrix

17 Noise correlation matrix estimators Cont’d Noisy signal correlation matrix

18 Noise correlation matrix estimators Cont’d GEVP

19 Noise correlation matrix estimators Cont’d GEVP Noisy signal correlation matrix decomposition

20 Noise correlation matrix estimators Cont’d Signal subspace

21 Noise correlation matrix estimators Cont’d Noise subspace

22 Noise correlation matrix estimators Cont’d

23

24

25 Noise correlation matrix estimation

26 Noise correlation matrix estimators Cont’d Noise correlation matrix estimation

27 Noise correlation matrix estimators Cont’d Noise correlation matrix estimation

28 Noise correlation matrix estimators Cont’d Noise correlation matrix estimation

29 Noise correlation matrix estimators Cont’d Noise correlation matrix estimation

30 Noise correlation matrix estimators Cont’d Noise correlation matrix estimation [Kamkar-Parsi and Bouchard, 2009]

31 Performance measure ▶ Other multichannel algorithms that can’t be included as a subcases involve knowledge on the sources directivities. Not fair! ▶ Single channel algorithms don’t use information on directivities. Comparison with single channel algorithms

32 Performance measure ▶ Problems with comparison: o Single channel algorithms estimate only diagonal elements of the correlation matrix Comparison with single channel algorithms

33 Performance measure Cont’d Comparison with reference noise correlation matrix

34 Performance measure Cont’d Channels Image courtesy of [Kayser et al., 2009]

35 Performance measure Cont’d Channels

36 Performance measure Cont’d ▶ TIMIT database used for the sentences ▶ Oldenburg university database use for diffuse noise and HRTFs Setup [Kayser et al., 2009] 1 2 3

37 Performance measure Cont’d Anechoic environment Log-error with constant SNRs

38 Performance measure Cont’d Anechoic environment Log-error time varying SNRs

39 Performance measure Cont’d Cafeteria environment Log-error with N=1 for binaural setting

40 Conclusion & Outlook


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