Title :
Mutual information approach to blind separation of stationary sources
Author_Institution :
Lab. of Modeling & Comput., CNRS, Grenoble, France
fDate :
7/1/2002 12:00:00 AM
Abstract :
This paper presents a unified approach to the problem of blind separation of sources, based on the concept of mutual information. This concept is applied to the whole source sequences as stationary processes and thus provides a universal contrast applicable to both the instantaneous and convolutive mixture cases. For practical implementation, we introduce several degraded forms of this contrast, computable from a finite-dimensional distribution of the reconstructed source processes only. From them, we derive several sets of estimating equations, generalizing those considered earlier
Keywords :
Gaussian processes; Markov processes; convolution; entropy; signal reconstruction; Gaussian processes; Markovian processes; blind separation; convolutive mixture; entropy; estimating equations; finite-dimensional distribution; instantaneous mixture; mutual information; reconstructed source processes; source sequences; stationary processes; stationary sources; Computational modeling; Convolution; Degradation; Distributed computing; Entropy; Equations; Image reconstruction; Independent component analysis; Mutual information; Proposals;
Journal_Title :
Information Theory, IEEE Transactions on
DOI :
10.1109/TIT.2002.1013134