A modeling and algorithmic framework for (non)social (co)sparse audio restoration

Abstract : We propose a unified modeling and algorithmic framework for audio restoration problem. It encompasses analysis sparse priors as well as more classical synthesis sparse priors, and regular sparsity as well as various forms of structured sparsity embodied by shrinkage operators (such as social shrinkage). The versatility of the framework is illustrated on two restoration scenarios: denoising, and declipping. Extensive experimental results on these scenarios highlight both the speedups of 20% or even more offered by the analysis sparse prior, and the substantial declipping quality that is achievable with both the social and the plain flavor. While both flavors overall exhibit similar performance, their detailed comparison displays distinct trends depending whether declipping or denoising is considered.
Type de document :
Pré-publication, Document de travail
2017
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https://hal.inria.fr/hal-01649261
Contributeur : Clément Gaultier <>
Soumis le : mercredi 29 novembre 2017 - 17:16:28
Dernière modification le : mardi 17 avril 2018 - 09:05:31

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  • HAL Id : hal-01649261, version 1
  • ARXIV : 1711.11259

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Clément Gaultier, Nancy Bertin, Srđan Kitić, Rémi Gribonval. A modeling and algorithmic framework for (non)social (co)sparse audio restoration. 2017. 〈hal-01649261〉

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