  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.
