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

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

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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

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Framework Noise correlation matrix models

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Framework Noise correlation matrix models Geometry dependent part

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Framework Noise correlation matrix models Geometry dependent part

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Framework Noise correlation matrix models Geometry dependent part scalar

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

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Framework Cont’d Noise correlation matrix models

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Framework Cont’d Noise correlation matrix models Geometry dependent part

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Framework Cont’d Noise correlation matrix models Geometry dependent part

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Framework Cont’d Noise correlation matrix models Geometry dependent part

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

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Framework Cont’d Models equivalence in special cases

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

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

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Noise correlation matrix estimators Cont’d Noisy signal correlation matrix

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Noise correlation matrix estimators Cont’d GEVP

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

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Noise correlation matrix estimators Cont’d Signal subspace

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Noise correlation matrix estimators Cont’d Noise subspace

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Noise correlation matrix estimators Cont’d

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Noise correlation matrix estimation

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Noise correlation matrix estimators Cont’d Noise correlation matrix estimation

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Noise correlation matrix estimators Cont’d Noise correlation matrix estimation

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Noise correlation matrix estimators Cont’d Noise correlation matrix estimation

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Noise correlation matrix estimators Cont’d Noise correlation matrix estimation

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

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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

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

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Performance measure Cont’d Comparison with reference noise correlation matrix

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

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Performance measure Cont’d Channels 2 4 1 3 56 7 8

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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

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

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

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

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Conclusion & Outlook

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