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Independent component analysis
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<p>In <a href="/facts/Signal_processing/Npaja6zb">signal processing</a>, independent component analysis (ICA) is a computational method for separating a <a href="/facts/Multivariate_statistics/tDuNcWrL">multivariate</a> signal into additive subcomponents. This is done by assuming that at most one subcomponent is Gaussian and that the subcomponents are <a href="/facts/Statistical_independence/NUzQtnUL">statistically independent</a> from each other. ICA was invented by Jeanny Hérault and Christian Jutten in 1985. ICA is a special case of <a href="/facts/Blind_source_separation/lrwEWUTC">blind source separation</a>. A common example application of ICA is the "<a href="/facts/Cocktail_party_problem/qnGLrtpr">cocktail party problem</a>" of listening in on one person's speech in a noisy room. </p>