Psychoacoustically Motivated Audio Declipping Based on Weighted l1 Minimization
Audio and Speech Processing
2020-07-02 v2 Sound
Abstract
A novel method for audio declipping based on sparsity is presented. The method incorporates psychoacoustic information by weighting the transform coefficients in the 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.
Keywords
Cite
@article{arxiv.1905.00628,
title = {Psychoacoustically Motivated Audio Declipping Based on Weighted l1 Minimization},
author = {Pavel Záviška and Pavel Rajmic and Jíří Schimmel},
journal= {arXiv preprint arXiv:1905.00628},
year = {2020}
}