Subband decomposition independent component analysis and new performance criteria

Toshihisa Tanaka, Andrzej Cichocki

Результат исследований: Вклад в журналСтатья конференциирецензирование

41 Цитирования (Scopus)

Аннотация

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.

Язык оригиналаАнглийский
Страницы (с-по)V-541-V-544
ЖурналICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Том5
СостояниеОпубликовано - 2004
Опубликовано для внешнего пользованияДа
СобытиеProceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Канада
Продолжительность: 17 мая 200421 мая 2004

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