Subband decomposition independent component analysis and new performance criteria

Toshihisa Tanaka, Andrzej Cichocki

Research output: Contribution to journalConference articlepeer-review

41 Citations (Scopus)

Abstract

We introduce a new extended model for independent component analysis (ICA) and/or blind source separation (BSS), in which the assumption of the standard ICA model that the source signals are mutually independent (or spatio-temporally uncorrelated) is relaxed. In the new model, the source is presumed to be the sum of some independent and/or dependent subcomponents. We show a practical solution for this class of blind separation problems by using the subband decomposition (SD) and the independence test by analyzing global rnixing-demixing matrices obtained for various subbands or multi-bands. This is very simple but efficient technique, and users just apply the proposed method to conventional ICA/BSS algorithms as pre- and post-processing. The method proposed in the paper has been tested for blind separation problems with partially dependent sources. The results indicate that the method is promising for the signal separation problem of speech, image, EEG data and so on.

Original languageEnglish
Pages (from-to)V-541-V-544
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume5
Publication statusPublished - 2004
Externally publishedYes
EventProceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada
Duration: 17 May 200421 May 2004

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