Audio source separation based on independent component analysis

Shoji Makino*, Shoko Araki, Ryo Mukai, Hiroshi Sawada

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

17 Citations (Scopus)


This paper introduces the blind source separation (BSS) of convolutive mixtures of acoustic signals, especially speech. A statistical and computational technique, called independent component analysis (ICA), is examined. By achieving nonlinear decorrelation, nonstationary decorrelation, or time-delayed decorrelation, we can find source signals only from observed mixed signals. Particular attention is paid to the physical interpretation of BSS from the acoustical signal processing point of view. Frequency-domain BSS is shown to be equivalent to two sets of frequency domain adaptive microphone arrays, i.e., adaptive beamformers (ABFs). Although BSS can reduce reverberant sounds to some extent in the same way as ABF, it mainly removes the sounds from the jammer direction. This is why BSS has difficulties with long reverberation in the real world. If sources are not "independent," the dependence results in bias noise when obtaining the correct unmixing filter coefficients. Therefore, the performance of BSS is limited by that of ABF. Although BSS is upper bounded by ABF, BSS has a strong advantage over ABF. BSS can be regarded as an intelligent version of ABF in the sense that it can adapt without any information on the array manifold or the target direction, and sources can be simultaneously active in BSS.

Original languageEnglish
Pages (from-to)V-668-V-671
JournalProceedings - IEEE International Symposium on Circuits and Systems
Publication statusPublished - 2004
Externally publishedYes
Event2004 IEEE International Symposium on Cirquits and Systems - Proceedings - Vancouver, BC, Canada
Duration: 2004 May 232004 May 26

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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