Journal Article

Bayesian approach for blind separation of underdetermined mixtures of sparse sources

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2004) 3195 398-405
DOI: 10.1007/978-3-540-30110-3_51
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Abstract

We address in this paper the problem of blind separation of underdetermined mixtures of sparse sources. The sources are given a Student t distribution, in a transformed domain, and we propose a bayesian approach using Gibbs sampling. Results are given on synthetic and audio signals. © Springer-Verlag 2004.

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Févotte, C., Godsill, S. J., & Wolfe, P. J. (2004). Bayesian approach for blind separation of underdetermined mixtures of sparse sources. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3195, 398–405. https://doi.org/10.1007/978-3-540-30110-3_51

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