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In signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents. This is done by assuming that at most one subcomponent is Gaussian and that the subcomponents are statistically independent from each other.^[1] ICA was invented by Jeanny Hérault and Christian Jutten in 1985.^[2] ICA is a special case of blind source separation. A common example application of ICA is the "cocktail party problem" of listening in on one person's speech in a noisy room.^[3]

^ "Independent Component Analysis: A Demo".
^ Cite error: The named reference jutten85 was invoked but never defined (see the help page).
^ Hyvärinen, Aapo (2013). "Independent component analysis: recent advances". Philosophical Transactions: Mathematical, Physical and Engineering Sciences. 371 (1984): 20110534. Bibcode:2012RSPTA.37110534H. doi:10.1098/rsta.2011.0534. ISSN 1364-503X. JSTOR 41739975. PMC 3538438. PMID 23277597.