An efficient measure of signal temporal predictability for blind source separation

Ye, M. and Li, X. (2007) An efficient measure of signal temporal predictability for blind source separation. Neural Processing Letters, 26 1: 57-68.


Author(s) Ye, M.
Li, X.
Title An efficient measure of signal temporal predictability for blind source separation
Journal name Neural Processing Letters
Publication date 2007
Sub-type Article
Volume number 26
Issue number 1
ISSN 1370-4621
Start page 57
End page 68
Total pages 12
Editor(s) de Poel, G.
Place of publication Dordrecht
Publisher Springer
Collection year 2008
Subject 280212 Neural Networks, Genetic Alogrithms and Fuzzy Logic
700000 - Information and Communication Services
C1
Abstract An efficient measure of signal temporal predictability is proposed, which is referred to as difference measure. We can prove that the difference measure of any signal mixture is between the maximal and minimal difference measure of the source signals. Previous blind source separation (BSS) problem is changed to a generalized eigenproblem by using Stone's measure. However, by using difference measure, the BSS problem is furthermore simplified to a standard symmetric eigenproblem. And the separation matrix is the eigenvector matrix, which can be solved by using principal component analysis (PCA) method. Based on difference measure, a few efficient algorithms have been proposed, which are either in batch mode or in on-line mode. Simulations show that difference measure is competitive with Stone's measure. Especially, the on-line algorithms derived from difference measure have better performance than those defived from Stone's measure.
Keyword(s) Computer Science, Artificial Intelligence
Neurosciences
blind source separation
principal component analysis
neural networks
eigenvector
Learning Algorithm
 
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