Separation of Non-Negative Mixture of Non-Negative Sources Using a Bayesian Approach and MCMC Sampling

Abstract : This paper addresses blind-source separation in the case where both the source signals and the mixing coefficients are non-negative. The problem is referred to as non-negative source separation and the main application concerns the analysis of spectrometric data sets. The separation is performed in a Bayesian framework by encoding non-negativity through the assignment of Gamma priors on the distributions of both the source signals and the mixing coefficients. A Markov chain Monte Carlo (MCMC) sampling procedure is proposed to simulate the resulting joint posterior density from which marginal posterior mean estimates of the source signals and mixing coefficients are obtained. Results obtained with synthetic and experimental spectra are used to discuss the problem of non-negative source separation and to illustrate the effectiveness of the proposed method.
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-00121602
Contributor : David Brie <>
Submitted on : Thursday, December 21, 2006 - 12:28:08 PM
Last modification on : Thursday, May 24, 2018 - 10:32:01 AM

Links full text

Identifiers

Citation

Saïd Moussaoui, David Brie, Ali Mohammad-Djafari, Cédric Carteret. Separation of Non-Negative Mixture of Non-Negative Sources Using a Bayesian Approach and MCMC Sampling. IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2006, 54 (11), pp.4133-4145. ⟨10.1109/TSP.2006.880310⟩. ⟨hal-00121602⟩

Share

Metrics

Record views

324