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Blind source separation using temporal predictability

Stone, J.V. (2001) Blind source separation using temporal predictability. Neural Computation, 13 (7). pp. 1559-1574. ISSN 0899-7667

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Abstract

A measure of temporal predictability is defined and used to separate linear mixtures of signals. Given any set of statistically independent source signals, it is conjectured here that a linear mixture of those signals has the following property: the temporal predictability of any signal mixture is less than (or equal to) that of any of its component source signals.

It is shown that this property can be used to recover source signals from a set of linear mixtures of those signals by finding an un-mixing matrix that maximizes a measure of temporal predictability for each recovered signal. This matrix is obtained as the solution to a generalized eigenvalue problem; such problems have scaling characteristics of O (N3), where N is the number of signal mixtures. In contrast to independent component analysis, the temporal predictability method requires minimal assumptions regarding the probability density functions of source signals. It is demonstrated that the method can separate signal mixtures in which each mixture is a linear combination of source signals with supergaussian, sub-gaussian, and gaussian probability density functions and on mixtures of voices and music.

Item Type: Article
Copyright, Publisher and Additional Information: © 2001 Massachusetts Institute of Technology. Reproduced in accordance with the publisher's self-archiving policy.
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Science (Sheffield) > Department of Psychology (Sheffield)
Depositing User: Repository Assistant
Date Deposited: 29 Jun 2006
Last Modified: 05 Jun 2014 20:46
Published Version: http://dx.doi.org/10.1162/089976601750265009
Status: Published
Publisher: MIT Press
Refereed: Yes
Identification Number: 10.1162/089976601750265009
URI: http://eprints.whiterose.ac.uk/id/eprint/1430

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