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

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

研究成果査読

66 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号4244514
ページ(範囲)1511-1520
ページ数10
ジャーナルIEEE Transactions on Audio, Speech and Language Processing
15
5
DOI
出版ステータスPublished - 2007 7
外部発表はい

ASJC Scopus subject areas

  • 音響学および超音波学
  • 電子工学および電気工学

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