Audio Inpainting: Revisited and Reweighted

Druh výsledku
článek v časopise ve Web of Science, Jimp

In this article, 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.

Klíčová slova
audio inpainting
sparse representations
Proximal Algorithms
Douglas–Rachford algorithm
Chambolle–Pock algorithm
energy loss compensation
amplitude drop