Revisiting Synthesis Model of Sparse Audio Declipper

Abstract

The state of the art in audio declipping has currently been achieved by SPADE (SParse Audio DEclipper) algorithm by Kitić et al. Until now, the synthesis/sparse variant, S-SPADE, has been considered significantly slower than its analysis/cosparse counterpart, A-SPADE. It turns out that the opposite is true: by exploiting a recent projection lemma, individual iterations of both algorithms can be made equally computationally expensive, while S-SPADE tends to require considerably fewer iterations to converge. In this paper, the two algorithms are compared across a range of parameters such as the window length, window overlap and redundancy of the transform. The experiments show that although S-SPADE typically converges faster, the average performance in terms of restoration quality is not superior to A-SPADE.

References in corpus (2)

• The Cosparse Analysis Model and Algorithms
• A modeling and algorithmic framework for (non)social (co)sparse audio restoration

Cited by in corpus (6)

• Sparse Recovery and Dictionary Learning from Nonlinear Compressive Measurements
• Introducing SPAIN (SParse Audio INpainter)
• Audio Inpainting: Revisited and Reweighted
• A Proper version of Synthesis-based Sparse Audio Declipper
• Psychoacoustically Motivated Audio Declipping Based on Weighted l1 Minimization
• Flexible framework for audio reconstruction