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| Independent component analysis (ICA) is a computational method
for separating a multivariate signal into additive subcomponents
supposing the mutual statistical independence of the non-
Gaussian source signals. It is a special case of blind source
separation. When the independence assumption is correct, blind
ICA separation of a mixed signal gives very good results. It is
also used for signals that are not supposed to be generated by a
mixing for analysis purposes. A simple application of ICA is the
“cocktail party problem”, where the underlying speech signals
are separated from a sample data consisting of people talking
simultaneously in a room. Usually the problem is simplified by
assuming no time delays and echoes. An important note to
consider is that if N sources are present, at least N
observations (e.g. microphones) are needed to get the original
signals. This constitutes the squ