Audio Inpainting: Revisited and Reweighted

Abstract

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.

References in corpus (3)

• Introducing SPAIN (SParse Audio INpainter)
• A Proper version of Synthesis-based Sparse Audio Declipper
• Revisiting Synthesis Model of Sparse Audio Declipper

Cited by in corpus (7)

• GACELA -- A generative adversarial context encoder for long audio inpainting
• A survey and an extensive evaluation of popular audio declipping methods
• Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization
• Multiple Hankel matrix rank minimization for audio inpainting
• Janssen 2.0: Audio Inpainting in the Time-frequency Domain
• Tweaking autoregressive methods for inpainting of gaps in audio signals
• Active Restoration of Lost Audio Signals Using Machine Learning and Latent Information