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

Clément Gaultier (1, 2) , Srđan Kitić (1, 2) , Rémi Gribonval (3, 1) , Nancy Bertin (1)

1 PANAMA - Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio
2 Orange Labs [Cesson-Sévigné]
3 DANTE - Dynamic Networks : Temporal and Structural Capture Approach

Recent advances in audio declipping have substan- tially improved the state of the art. Yet, practitioners need guidelines to choose a method, and while existing benchmarks have been instrumental in advancing the field, larger-scale exper- iments 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.

Mots clés: audio declipping; time-frequency; structured sparsity; listening test; sparsity

Domaines: Son [cs.SD]; Traitement du signal et de l'image [eess.SP]

Clément Gaultier, Srđan Kitić, Rémi Gribonval, Nancy Bertin. Sparsity-based audio declipping methods: selected overview, new algorithms, and large-scale evaluation. IEEE/ACM Transactions on Audio, Speech and Language Processing, 2021, 29, pp.1174-1187. ⟨10.1109/TASLP.2021.3059264⟩. ⟨hal-02611226v3⟩