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.
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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 〉
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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〉

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