Audio Declipping with Social Sparsity

Abstract : We consider the audio declipping problem by using iterative thresholding algorithms and the principle of social sparsity. This recently introduced approach features thresholding/shrinkage operators which allow to model dependencies between neighboring coefficients in expansions with time-frequency dictionaries. A new unconstrained convex formulation of the audio declipping problem is introduced. The chosen structured thresholding operators are the so called \emph{windowed group-Lasso} and the \emph{persistent empirical Wiener}. The usage of these operators significantly improves the quality of the reconstruction, compared to simple soft-thresholding. The resulting algorithm is fast, simple to implement, and it outperforms the state of the art in terms of signal to noise ratio.
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Communication dans un congrès
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), May 2014, Florence, Italy. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing 2014, pp.AASP-L2, 2014
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Kai Siedenburg, Matthieu Kowalski, Monika Dörfler. Audio Declipping with Social Sparsity. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), May 2014, Florence, Italy. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing 2014, pp.AASP-L2, 2014. 〈hal-01002998〉

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