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| Paper: | MLSP-L2.3 |
| Session: | Blind Source Separation |
| Time: | Friday, May 21, 13:40 - 14:00 |
| Presentation: |
Lecture |
| Topic: |
Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis |
| Title: |
A BAYESIAN METHOD FOR POSITIVE SOURCE SEPARATION |
| Authors: |
Saïd Moussaoui; CRAN CNRS UMR 7039 UHP | | |
| | Ali Mohammad-Djafari; LSS-Supelec-Universite Paris-Sud | | |
| | David Brie; CRAN CNRS UMR 7039 UHP | | |
| | Olivier Caspary; CRAN CNRS UMR 7039 UHP | | |
| Abstract: |
This paper considers the problem of source separation in the particular case where both the sources and the mixingcoefficients are positive. The proposed method addresses the problem in a Bayesian framework. We assume a Gamma distribution for the spectra and the mixing coefficients. This prior distribution enforces the non-negativity. This leads to an original method for positive source separation. A simulation example is presented to illustrate the effectiveness of the method. |
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