Článek v odborném periodiku

Koldovský Z., Málek J., Tichavský P., Deville Y., Hosseini S

Signal Processing

2009

12 (2009)

2570-2584

0165-1684

Ing. Petr Tichavský, CSc. (ÚTIA - Oddělení stochastické informatiky)

Independent component analysis, blind source separation, Cra

We address Independent Component Analysis (ICA) of piecewise stationary and nonGaussian signals and propose a novel ICA algorithm called Block EFICA that is based on this generalized model of signals. The method is a further extension of the popular nonGaussianity-based FastICA algorithm and of its recently optimized variant called EFICA. In contrast to these methods, Block EFICA is developed to effectively exploit varying distribution of signals, thus, also their varying variance in time (nonstationarity) or, more precisely, in time-intervals (piecewise stationarity). In theory, the accuracy of the method asymptotically approaches Cramer-Rao lower bound (CRLB) under common assumptions when variance of the signals is constant. On the other hand, the performance is practically close to the CLRB even when variance of the signals is changing.