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

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4 Author(s)
Dimitri Nion ; Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania, Greece ; Kleanthis N. Mokios ; Nicholas D. Sidiropoulos ; Alexandros Potamianos

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.

Published in:

IEEE Transactions on Audio, Speech, and Language Processing  (Volume:18 ,  Issue: 6 )