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.
Liste complète des métadonnées

Cited literature [28 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01002998
Contributor : Matthieu Kowalski <>
Submitted on : Sunday, June 8, 2014 - 3:21:59 PM
Last modification on : Thursday, February 7, 2019 - 3:08:52 PM
Document(s) archivé(s) le : Monday, September 8, 2014 - 10:36:53 AM

File

SDK_icassp14.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01002998, version 1

Collections

Citation

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⟩

Share

Metrics

Record views

391

Files downloads

636