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Blind Separation of Sources: Methods, Assumptions and Applications
Ali MANSOUR Allan Kardec BARROS Noboru OHNISHI
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2000/08/25
Print ISSN: 0916-8508
Type of Manuscript: SURVEY PAPER (Special Section on Digital Signal Processing)
independent component analysis (ICA), contrast function, Kullback-Leibner divergence, prediction error, sub-space, decorrelation, high order statistics, whitening, Mutual-Information, likelihood maximization, conjoint diagonalization,
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The blind separation of sources is a recent and important problem in signal processing. Since 1984, it has been studied by many authors whilst many algorithms have been proposed. In this paper, the description of the problem, its assumptions, its currently applications and some algorithms and ideas are discussed.