Psychoacoustically Motivated Audio Declipping Based on Weighted l1 Minimization
This work addresses audio quality restoration for applications like audio processing and communication, but it is incremental as it builds on existing sparsity and psychoacoustic techniques.
The authors tackled audio declipping by proposing a sparsity-based method that incorporates psychoacoustic weighting in l1 minimization, resulting in improved restoration quality that can compete with or outperform state-of-the-art methods, as measured by SDR and ODG metrics.
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.