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Sparsity-based audio declipping methods: overview, new algorithms, and large-scale evaluation

Clément Gaultier 1 Srđan Kitić 2 Rémi Gribonval 1, 3 Nancy Bertin 1
1 PANAMA - Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio
Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
3 DANTE - Dynamic Networks : Temporal and Structural Capture Approach
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme, IXXI - Institut Rhône-Alpin des systèmes complexes
Abstract : Recent advances in audio declipping have substantially improved the state of the art in certain saturation regimes. Yet, practitioners need guidelines to choose a method, and while existing benchmarks have been instrumental in advancing the field, larger-scale experiments are needed to guide such choices. First, we show that the saturation levels in existing small-scale benchmarks are moderate and call for benchmarks with more perceptually significant saturation levels. We then propose a general algorithmic framework for declipping that covers existing and new combinations of flavors of state-of-the-art techniques exploiting time-frequency sparsity: synthesis vs analysis sparsity, with plain or structured sparsity. Finally, we systematically compare these combinations and state-of-the-art methods. Using a large-scale numerical benchmarks and a smaller scale formal listening test, we provide guidelines for various saturation levels, both for speech and various musical “genres” from the RWC database. The code is made publicly available for the purpose of reproducible research and benchmarking.
Complete list of metadatas

https://hal.inria.fr/hal-02611226
Contributor : Clément Gaultier <>
Submitted on : Monday, May 18, 2020 - 6:04:47 PM
Last modification on : Thursday, May 21, 2020 - 1:19:01 AM

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Declip2020.pdf
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Identifiers

  • HAL Id : hal-02611226, version 1
  • ARXIV : 2005.10228

Citation

Clément Gaultier, Srđan Kitić, Rémi Gribonval, Nancy Bertin. Sparsity-based audio declipping methods: overview, new algorithms, and large-scale evaluation. 2020. ⟨hal-02611226⟩

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