Paper Available --- Blind Source Separation

[Barak Pearlmutter]

The following paper (which will appear at the 1996 International
Conference on Neural Information Processing this Fall) is available as
http://www.cnl.salk.edu/~bap/papers/iconip-96-cica.ps.gz


	      A Context-Sensitive Generalization of ICA

	       Barak A. Pearlmutter and Lucas C. Parra

			       Abstract

Source separation arises in a surprising number of signal processing
applications, from speech recognition to EEG analysis.  In the square
linear blind source separation problem without time delays, one must
find an unmixing matrix which can detangle the result of mixing $n$
unknown independent sources through an unknown $n \times n$ mixing
matrix.  The recently introduced ICA blind source separation algorithm
(Baram and Roth 1994; Bell and Sejnowski 1995) is a powerful and
surprisingly simple technique for solving this problem.  ICA is all
the more remarkable for performing so well despite making absolutely
no use of the temporal structure of its input!  This paper presents a
new algorithm, contextual ICA, which derives from a maximum likelihood
density estimation formulation of the problem.  cICA can incorporate
arbitrarily complex adaptive history-sensitive source models, and
thereby make use of the temporal structure of its input.  This allows
it to separate in a number of situations where standard ICA cannot,
including sources with low kurtosis, colored gaussian sources, and
sources which have gaussian histograms.  Since ICA is a special case
of cICA, the MLE derivation provides as a corollary a rigorous
derivation of classic ICA.