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Determination of the number of sources in blind source separation
Author(s) -
Mahieddine M. Ichir
Publication year - 2005
Publication title -
aip conference proceedings
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.177
H-Index - 75
eISSN - 1551-7616
pISSN - 0094-243X
DOI - 10.1063/1.2149804
Subject(s) - a priori and a posteriori , blind signal separation , computer science , source separation , separation (statistics) , sampling (signal processing) , bayesian probability , algorithm , selection (genetic algorithm) , computation , bayes' theorem , mathematical optimization , mathematics , artificial intelligence , machine learning , computer network , channel (broadcasting) , philosophy , epistemology , filter (signal processing) , computer vision
The determination of the number (n) of unobserved sources is an important issue in Blind Source Separation (BSS) of linear and instantaneous mixtures. However BSS is already a difficult task, so we generally assume that this number (n) is known and a priori fixed. In this paper, we address this issue as a Bayesian model selection problem and view the determination of this number (n) as a hypothesis testing problem via comparison of Bayes factors and study the computation of these factors by two numerical approximations: importance sampling from the posterior and simulated annealing sampling.Seeking for a general solution may be tricky to this problem, so we will be interested in blind separation of sparse sources modeled by a double exponential prior “π(.) ∝ exp(−λ|.|)”.

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