Batch and adaptive PARAFAC-based blind separation of convolutive speech mixtures

Dimitri Nion, Kleanthis N. Mokios, Nicholas D. Sidiropoulos, Alexandros Potamianos

Research output: Contribution to journalArticlepeer-review

92 Scopus citations


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 languageEnglish (US)
Article number5233821
Pages (from-to)1193-1207
Number of pages15
JournalIEEE Transactions on Audio, Speech and Language Processing
Issue number6
StatePublished - 2010
Externally publishedYes

Bibliographical note

Funding 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


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