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

Fangchen Feng 1 Matthieu Kowalski 1
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.
Liste complète des métadonnées

Cited literature [17 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01002996
Contributor : Matthieu Kowalski <>
Submitted on : Tuesday, June 10, 2014 - 12:13:03 PM
Last modification on : Saturday, September 8, 2018 - 4:24:02 PM
Document(s) archivé(s) le : Wednesday, September 10, 2014 - 11:12:02 AM

File

FK_icassp14.pdf
Files produced by the author(s)

Identifiers

Collections

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〉

Share

Metrics

Record views

385

Files downloads

263