Audio Inpainting: Revisited and Reweighted
This work addresses energy loss in audio inpainting for applications like audio restoration, but it is incremental as it builds on existing sparsity and convex optimization frameworks.
The paper tackled the problem of insufficient amplitude in sparsity-based audio inpainting by proposing improvements based on weighting in coefficient and time domains, resulting in enhanced performance with gains in SNR and ODG metrics.
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.