A Bayesian approach to blind separation of sparse sources
By: Researcher II
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| Authors | C. Févotte & S.J. Godsill |
| Journal/Conference Name | IEEE Transactions on Audio, Speech and Language Processing |
| Paper Category | ECE |
| Paper Abstract | We present a Bayesian approach for blind separation of linear instantaneous mixtures of sources having a sparse representation in a given basis. The distributions of the coefficients of the sources in the basis are modeled by a Student t distribution, which can be expressed as a Scale Mixture of Gaussians, and a Gibbs sampler is derived to estimate the sources, the mixing matrix, the input noise variance and also the hyperparameters of the Student t distributions. The method allows for separation of underdetermined (more sources than sensors) noisy mixtures. Results are presented with audio signals using a Modified Discrete Cosine Transfrom basis and compared with a finite mixture of Gaussians prior approach. These results show the improved sound quality obtained with the Student t prior and the better robustness to mixing matrices close to singularity of the Markov Chains Monte Carlo approach. |
| Date of publication | 2006 |
| Code Programming Language | MATLAB |
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