Blind separation of sparse sources using variational EM

In this paper, we tackle the general linear instantaneous model (possibly underdetermined and noisy) using the assumption of sparsity of the sources on a given dictionary. We model the sparsity of expansion coefficients with a Student t prior. The conjugate-exponential characterisation of the t dist...

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Vydáno v:13th European Signal Processing Conference (EUSIPCO 2005) s. 1 - 4
Hlavní autoři: Cemgil, Ali Taylan, Fevotte, Cedric, Godsill, Simon J.
Médium: Konferenční příspěvek Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.09.2005
Témata:
Algorithms
Approximation methods
Bayes methods
Blind source separation
Blinds
Dictionaries
Gaussian
Monte Carlo methods
Noise
Noise measurement
Separation
Signal processing
Students
Transforms
ISBN:1604238216, 9781604238211
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Shrnutí:In this paper, we tackle the general linear instantaneous model (possibly underdetermined and noisy) using the assumption of sparsity of the sources on a given dictionary. We model the sparsity of expansion coefficients with a Student t prior. The conjugate-exponential characterisation of the t distribution as an infinite mixture of scaled Gaussians enables us to derive an efficient variational expectation maximisation algorithm (V-EM). The resulting deterministic algorithm has superior properties in terms of computation time and achieves a separation performance comparable in quality to alternative methods based on Markov Chain Monte Carlo (MCMC).
Bibliografie:ObjectType-Article-2
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SourceType-Conference Papers & Proceedings-2
ISBN:1604238216
9781604238211