  We deal with the problem of sparsity-based audio inpainting, i.e. filling in
the missing segments of audio. A consequence of the approaches based on
mathematical optimization is the insufficient amplitude of the signal in the
filled gaps. Remaining in the framework based on sparsity and convex
optimization, we propose improvements to audio inpainting, aiming at
compensating for such an energy loss. The new ideas are based on different
types of weighting, both in the coefficient and the time domains. We show that
our propositions improve the inpainting performance in terms of both the SNR
and ODG.
