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.
|Original language||English (US)|
|Number of pages||15|
|Journal||IEEE Transactions on Audio, Speech and Language Processing|
|State||Published - 2010|
Bibliographical noteFunding Information:
Manuscript received June 24, 2008; revised July 31, 2009. First published September 09, 2009; current version published July 14, 2010. The work of D. Nion was supported by a postdoctoral grant from the Délégation Générale pour l’Armement (DGA) via ETIS Lab., UMR 8051 (ENSEA, CNRS, University of Cergy-Pontoise), France. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Jingdong Chen.
- Adaptive separation
- PARAllel FACtor (PARAFAC)
- blind speech separation
- joint diagonalization
- permutation ambiguity
- underdetermined case