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Springer, Lecture Notes in Computer Science, p. 243-250, 2015

DOI: 10.1007/978-3-319-22482-4_28

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Sparsity and Cosparsity for Audio Declipping: A Flexible Non-convex Approach

Journal article published in 2015 by Srdan Kitic, An Kitić, Nancy Bertin, Remi Gribonval
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This paper is available in a repository.

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Abstract

This work investigates the empirical performance of the sparse synthesis versus sparse analysis regularization for the ill-posed inverse problem of audio declipping. We develop a versatile non-convex heuristics which can be readily used with both data models. Based on this algorithm, we report that, in most cases, the two models perform almost similarly in terms of signal enhancement. However, the analysis version is shown to be amenable for real time audio processing, when certain analysis operators are considered. Both versions outperform state-of-the-art methods in the field, especially for the severely saturated signals.