Hybrid model and structured sparsity for under-determined convolutive audio source separation

1 Division Signaux - L2S
L2S - Laboratoire des signaux et systèmes : 1289
Abstract : We consider the problem of extracting the source signals from an under-determined convolutive mixture, assuming known filters. We start from its formulation as a minimization of a convex functional, combining a classical $\ell_2$ discrepancy term between the observed mixture and the one reconstructed from the estimated sources, and a sparse regularization term of source coefficients in a time-frequency domain. We then introduce a first kind of structure, using a hybrid model. Finally, we embed the previously introduced Windowed-Group-Lasso operator into the iterative thresholding/shrinkage algorithm, in order to take into account some structures inside each layers of time-frequency representations. Intensive numerical studies confirm the benefits of such an approach.
Type de document :
Communication dans un congrès
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), May 2014, Florence, Italy. pp.AASP-P9.9, 2014, 〈10.1109/icassp.2014.6854893 〉
Domaine :
Liste complète des métadonnées

Littérature citée [17 références]

https://hal.archives-ouvertes.fr/hal-01002996
Contributeur : Matthieu Kowalski <>
Soumis le : mardi 10 juin 2014 - 12:13:03
Dernière modification le : samedi 8 septembre 2018 - 16:24:02
Document(s) archivé(s) le : mercredi 10 septembre 2014 - 11:12:02

Fichier

FK_icassp14.pdf
Fichiers produits par l'(les) auteur(s)

Citation

Fangchen Feng, Matthieu Kowalski. Hybrid model and structured sparsity for under-determined convolutive audio source separation. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), May 2014, Florence, Italy. pp.AASP-P9.9, 2014, 〈10.1109/icassp.2014.6854893 〉. 〈hal-01002996〉

Métriques

Consultations de la notice

380

Téléchargements de fichiers