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TASLP
2010

Batch and Adaptive PARAFAC-Based Blind Separation of Convolutive Speech Mixtures

8 years 5 months ago
Batch and Adaptive PARAFAC-Based Blind Separation of Convolutive Speech Mixtures
We present a frequency-domain technique based on PARAllel FACtor (PARAFAC) analysis that performs multichannel blind source separation (BSS) of convolutive speech mixtures. PARAFAC algorithms are combined with a dimensionality reduction step to significantly reduce computational complexity. The identifiability potential of PARAFAC is exploited to derive a BSS algorithm for the under-determined case (more speakers than microphones), combining PARAFAC analysis with time-varying Capon beamforming. Finally, a low-complexity adaptive version of the BSS algorithm is proposed that can track changes in the mixing environment. Extensive experiments with realistic and measured data corroborate our claims, including the under-determined case. Signal-to-interference ratio improvements of up to 6 dB are shown compared to state-of-the-art BSS algorithms, at an order of magnitude lower computational complexity.
Dimitri Nion, Kleanthis N. Mokios, Nicholas D. Sid
Added 21 May 2011
Updated 21 May 2011
Type Journal
Year 2010
Where TASLP
Authors Dimitri Nion, Kleanthis N. Mokios, Nicholas D. Sidiropoulos, Alexandros Potamianos
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