Blind Separation of Piecewise Stationary NonGaussian Sources

0332915 - ÚTIA 2010 RIV NL eng J - Journal Article
Koldovský, Zbyněk - Málek, J. - Tichavský, Petr - Deville, Y. - Hosseini, S.
Blind Separation of Piecewise Stationary NonGaussian Sources.
[Slepá separace po částech stacionárních negaussovských zdrojů.]
Signal Processing. Roč. 89, č. 12 (2009), s. 2570-2584. ISSN 0165-1684. E-ISSN 1872-7557
R&D Projects: GA MŠMT 1M0572; GA ČR GA102/09/1278
Grant - others:GA ČR(CZ) GA102/07/P384
Institutional research plan: CEZ:AV0Z10750506
Keywords : Independent component analysis * blind source separation * Cramer-Rao lower bound
Subject RIV: BB - Applied Statistics, Operational Research
Impact factor: 1.135, year: 2009
http://library.utia.cas.cz/separaty/2009/SI/tichavsky-blind separationofpiecewisestationarynon-gaussiansources.pdf

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.

V práci je navržen algoritmus pro slepou separaci lineární okamžité směsi nezávislých po částech stacionárních negaussovských zdrojů. Algoritmus je nazván "bloková EFICA" a je rozšířením a zobecněním dřívějších algorimů FastICA a EFICA. V případě, že separované signálz mají konstantní varianci, dosahuje přesnost separace asymptoticky příslušné Rao-Cramerovy meze. Přínos algoritmu je ukázán na separaci okamžité směsi zvukových signálů.
Permanent Link: http://hdl.handle.net/11104/0178031