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

Clément Gaultier 1, 2 Srđan Kitić 1, 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 clipping levels in existing small-scale benchmarks are moderate and call for benchmarks with more perceptually significant clipping levels. We then propose a general algorithmic framework for declipping that covers existing and new combinations of variants 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 a selection of state-of-the-art methods. Using a large-scale numerical benchmark and a smaller scale formal listening test, we provide guidelines for various clipping levels, both for speech and various musical genres. The code is made publicly available for the purpose of reproducible research and benchmarking.
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Preprints, Working Papers, ...
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https://hal.inria.fr/hal-02611226
Contributor : Rémi Gribonval <>
Submitted on : Monday, November 30, 2020 - 11:43:39 AM
Last modification on : Tuesday, December 1, 2020 - 3:45:25 AM

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  • HAL Id : hal-02611226, version 2
  • ARXIV : 2005.10228

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Clément Gaultier, Srđan Kitić, Rémi Gribonval, Nancy Bertin. Sparsity-based audio declipping methods: selected overview, new algorithms, and large-scale evaluation. 2020. ⟨hal-02611226v2⟩

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