Independent Component Analysis
نویسنده
چکیده
Abstract: Given a set of M signal mixtures (x1, x2, . . . , xM ) (e.g. microphone outputs), each of which is a different mixture of a set of M statistically independent source signals (s1, s2, . . . , sM ) (e.g. voices), independent component analysis (ICA) recovers the source signals (voices) from the signal mixtures. ICA is based on the assumptions that source signals are statistically independent and that they have non-Gaussian distributions. Different physical processes usually generate statistically independent and non-Gaussian signals, so that, in the process of extracting such signals from a set of signal mixtures, ICA effectively recovers the underlying physical causes for a given set of measured signal mixtures.
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