A modeling and algorithmic framework for (non)social (co)sparse audio restoration - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2017

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

Résumé

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.
Fichier principal
Vignette du fichier
main.pdf (1.34 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-01649261 , version 1 (29-11-2017)

Identifiants

Citer

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

Relations

664 Consultations
156 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More