Name
Abstract | ||
|---|---|---|
We consider the blind source separation (BSS) problem in the noisy context. We propose a new methodology in order to enhance separation performances in terms of efficiency and robustness. Our approach consists in denoising the observed signals through the minimization of their total variation, and then minimizing divergence separation criteria combined with the total variation of the estimated source signals. We show by the way that the method leads to some projection problems that are solved by means of projected gradient algorithms. The efficiency and robustness of the proposed algorithm using Hellinger divergence are illustrated and compared with the classical mutual information approach through numerical simulations. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.sigpro.2012.09.026 | Signal Processing |
Keywords | Field | DocType |
separation performance,hellinger divergence,divergence separation criterion,total variation,new methodology,noisy context,classical mutual information approach,robust algorithm,convolutive blind source separation,numerical simulation,blind source separation,estimated source signal | Noise reduction,Stochastic optimization,Mathematical optimization,Divergence,Pattern recognition,Algorithm,Robustness (computer science),Minification,Artificial intelligence,Mutual information,Blind signal separation,Mathematics | Journal |
Volume | Issue | ISSN |
93 | 4 | 0165-1684 |
Citations | PageRank | References |
7 | 0.54 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. El Rhabi | 1 | 7 | 0.54 |
| H. Fenniri | 2 | 7 | 0.54 |
| Amor Keziou | 3 | 31 | 5.57 |
| E. Moreau | 4 | 282 | 25.26 |