Speech source separation in convolutive environments using space-time-frequency analysis

Authors:
Shlomo Dubnov;Joseph Tabrikian;Miki Arnon-Targan
Affiliations:
CALIT 2, University of California, San Diego, CA;Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel;Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2006

Citing 2
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Abstract

We propose a new method for speech source separation that is based on directionally-disjoint estimation of the transfer functions between microphones and sources at different frequencies and at multiple times. The spatial transfer functions are estimated from eigenvectors of the microphones' correlation matrix. Smoothing and association of transfer function parameters across different frequencies are performed by simultaneous extended Kalman filtering of the amplitude and phase estimates. This approach allows transfer function estimation even if the number of sources is greater than the number of microphones, and it can operate for both wideband and narrowband sources. The performance of the proposed method was studied via simulations and the results show good performance.