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arXiv · 1905.00628

Psychoacoustically Motivated Audio Declipping Based on Weighted l1 Minimization

Pavel Záviška·Pavel Rajmic·Jíří Schimmel
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Abstract

A novel method for audio declipping based on sparsity is presented. The method incorporates psychoacoustic information by weighting the transform coefficients in the $\ell_1$ minimization. Weighting leads to an improved quality of restoration while retaining a low complexity of the algorithm. Three possible constructions of the weights are proposed, based on the absolute threshold of hearing, the global masking threshold and on a quadratic curve. Experiments compare the restoration quality according to the signal-to-distortion ratio (SDR) and PEMO-Q objective difference grade (ODG) and indicate that with correctly chosen weights, the presented method is able to compete, or even outperform, the current state of the art.

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Pavel Záviška, Pavel Rajmic, Jíří Schimmel. 2019-05-02. Psychoacoustically Motivated Audio Declipping Based on Weighted l1 Minimization. https://doi.org/10.1109/tsp.2019.8769109

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