| 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. | ||
| Back | |||
Home -||-
Organizing Committee -||-
Technical Committee -||-
Technical Program -||-
Plenaries
Paper Submission -||-
Special Sessions -||-
ITT -||-
Paper Review -||-
Exhibits -||-
Tutorials
Information -||-
Registration -||-
Travel Insurance -||-
Housing -||-
Workshops