A Proper version of Synthesis-based Sparse Audio Declipper

Abstract

Methods based on sparse representation have found great use in the recovery of audio signals degraded by clipping. The state of the art in declipping has been achieved by the SPADE algorithm by Kitić et. al. (LVA/ICA2015). Our recent study (LVA/ICA2018) has shown that although the original S-SPADE can be improved such that it converges significantly faster than the A-SPADE, the restoration quality is significantly worse. In the present paper, we propose a new version of S-SPADE. Experiments show that the novel version of S-SPADE outperforms its old version in terms of restoration quality, and that it is comparable with the A-SPADE while being even slightly faster than A-SPADE.

References in corpus (2)

• Revisiting Synthesis Model of Sparse Audio Declipper
• S-SPADE Done Right: Detailed Study of the Sparse Audio Declipper Algorithms

Cited by in corpus (7)

• A survey and an extensive evaluation of popular audio declipping methods
• VoiceFixer: A Unified Framework for High-Fidelity Speech Restoration
• Introducing SPAIN (SParse Audio INpainter)
• Audio Inpainting: Revisited and Reweighted
• Psychoacoustically Motivated Audio Declipping Based on Weighted l1 Minimization
• Audio Dequantization Using (Co)Sparse (Non)Convex Methods
• Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization