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This work investigates the empirical performance of the sparse synthesisversus sparse analysis regularization for the ill-posed inverse problem ofaudio declipping. We develop a versatile non-convex heuristics which can bereadily used with both data models. Based on this algorithm, we report that, inmost cases, the two models perform almost similarly in terms of signalenhancement. However, the analysis version is shown to be amenable for realtime audio processing, when certain analysis operators are considered. Bothversions outperform state-of-the-art methods in the field, especially for theseverely saturated signals.

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