Independent Component Analysis
Abstract: Given a set of M signal mixtures (x 1 ;x 2 ;:::;x M ) (e.g. microphone out- puts), each of which is a dierent mixture of a set of M statistically independent source signals (s 1 ;s 2 ;:::;s M ) (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 dis- tributions. Dierent 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 eectively recovers the underlying physical causes for a given set of measured signal mixtures.