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Blind Separation of Mixture of Independent Sources Through a Maximum Likelihood Approach
 In Proc. EUSIPCO
, 1997
"... In this paper we propose two methods for separating mixtures of independent sources without any precise knowledge of their probability distribution. They are obtained by considering a maximum likelihood solution corresponding to some given distributions of the sources and relaxing this assumption af ..."
Abstract

Cited by 99 (8 self)
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In this paper we propose two methods for separating mixtures of independent sources without any precise knowledge of their probability distribution. They are obtained by considering a maximum likelihood solution corresponding to some given distributions of the sources and relaxing this assumption afterward. The first method is specially adapted to temporally independent non Gaussian sources and is based on the use of nonlinear separating functions. The second method is specially adapted to correlated sources with distinct spectra and is based on the use of linear separating filters. A theoretical analysis of the performance of the methods has been made. A simple procedure for choosing optimally the separating functions from a given linear space of functions is proposed. Further, in the second method, a simple implementation based on the simultaneous diagonalization of two symmetric matrices is provided. Finally, some numerical and simulation results are given illustrating the performan...
Mutual Information Approach to Blind Separation of Stationary Sources
 IEEE Transactions on Information Theory
, 1999
"... This paper presents an unified approach to the problem of separation of sources, based on the consideration of mutual information. The basic setup is that the sources are independent stationary random processes which are mixed either instantaneously or through a convolution, to produce the observed ..."
Abstract

Cited by 34 (8 self)
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This paper presents an unified approach to the problem of separation of sources, based on the consideration of mutual information. The basic setup is that the sources are independent stationary random processes which are mixed either instantaneously or through a convolution, to produce the observed records. We define the entropy of stationary processes and then the mutual information between them as a measure of their independence. This provides us with a contrast for the separation of source problem. For practical implementation, we introduce several degraded forms of this contrast, which can be computed from a finite dimensional distribution of the reconstructed source processes only. From them, we derive several sets of estimating equations generalising those considered earlier. 1 Introduction Blind separation of sources is a topic which have received much attention recently, as it has many important applications (speech analysis, radar, sonar, : : : ). Basically, one observes seve...