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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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Submitted on : Sunday, June 8, 2014 - 3:21:59 PM
Last modification on : Thursday, June 17, 2021 - 3:48:40 AM
Long-term archiving on: : Monday, September 8, 2014 - 10:36:53 AM


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  • HAL Id : hal-01002998, version 1



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. pp.AASP-L2. ⟨hal-01002998⟩



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