| Paper: | MLSP-P1.5 | ||
| Session: | Blind Source Separation and ICA | ||
| Time: | Tuesday, May 18, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis | ||
| Title: | SUBBAND DECOMPOSITION INDEPENDENT COMPONENT ANALYSIS AND NEW PERFORMANCE CRITERIA | ||
| Authors: | Toshihisa Tanaka; Brain Science Institute, RIKEN | ||
| Andrzej Cichocki; Brain Science Institute, RIKEN | |||
| 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 mixing-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. | ||
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