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Sparsity and cosparsity for audio declipping: a flexible non-convex approach

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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.
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Dates and versions

hal-01159700 , version 1 (03-06-2015)
hal-01159700 , version 2 (08-06-2015)

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Srđan Kitić, Nancy Bertin, Rémi Gribonval. Sparsity and cosparsity for audio declipping: a flexible non-convex approach. LVA/ICA 2015 - The 12th International Conference on Latent Variable Analysis and Signal Separation, Aug 2015, Liberec, Czech Republic. pp.8. ⟨hal-01159700v2⟩
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