Spatio-temporal FastICA algorithms for the blind separation of convolutive mixtures

Scott C. Douglas, Malay Gupta, Hiroshi Sawada, Shoji Makino

Research output: Contribution to journalArticlepeer-review

70 Citations (Scopus)


This paper derives two spatio-temporal extensions of the well-known FastICA algorithm of Hyvarinen and Oja that are applicable to the convolutive blind source separation task. Our time-domain algorithms combine multichannel spatio-temporal prewhitening via multistage least-squares linear prediction with novel adaptive procedures that impose paraunitary constraints on the multichannel separation filter. The techniques converge quickly to a separation solution without any step size selection or divergence difficulties, and unlike other methods, ours do not require special coefficient initialization procedures to obtain good separation performance. They also allow for the efficient reconstruction of individual signals as observed in the sensor measurements directly from the system parameters for single-input multiple-output blind source separation tasks. An analysis of one of the adaptive constraint procedures shows its fast convergence to a paraunitary filter bank solution. Numerical evaluations of the proposed algorithms and comparisons with several existing convolutive blind source separation techniques indicate the excellent relative performance of the proposed methods.

Original languageEnglish
Article number4244514
Pages (from-to)1511-1520
Number of pages10
JournalIEEE Transactions on Audio, Speech and Language Processing
Issue number5
Publication statusPublished - 2007 Jul
Externally publishedYes


  • Blind source separation (BSS)
  • Fixed-point algorithm
  • Independent component analysis
  • Speech enhancement

ASJC Scopus subject areas

  • Acoustics and Ultrasonics
  • Electrical and Electronic Engineering


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