AUDIO DECLIPPING WITH SOCIAL SPARSITYReport as inadecuate




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1 Schulich School of Music 2 Division Signal, GPI L2S - Laboratoire des signaux et systèmes 3 NuHAG - Numerical Harmonic Analysis Group, Faculty of Mathematics

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.





Author: Kai Siedenburg - Matthieu Kowalski - Monika Dörfler -

Source: https://hal.archives-ouvertes.fr/



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