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Summary: Blind Separation of Anechoic Under-determined
Speech Mixtures using Multiple Sensors
Rayan Saab1, ĻOzgĻur Yilmaz2, Martin J. McKeown3, Rafeef Abugharbieh1
1
Department of Electrical and Computer Engineering, The University of British Columbia.
2
Department of Mathematics, The University of British Columbia.
3
Department of Medicine (Neurology), Pacific Parkinson's Research Centre, The University of British Columbia
Abstract-- This paper presents a novel technique for Blind Source
Separation (BSS) of anechoic speech mixtures in the underdetermined
case. A demixing algorithm that exploits the sparsity of the short
time Fourier transform (STFT) of speech signals is proposed. The
algorithm merges constrained optimization with ideas based on the
degenerate unmixing estimation technique (DUET) [1]. Thus, the
novelty in the proposed approach is twofold. First, the algorithm
utilizes all available mixtures in the anechoic scenario, where both
attenuations and arrival delays between sensors are considered. Sec-
ond, it is demonstrated that lq
minimization with q < 1 outperforms
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