# Audio Inpainting: Revisited and Reweighted

arXiv: https://arxiv.org/abs/2001.02480 · HTML: https://mlsanity.com/paper/2001.02480/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models](https://mlsanity.com/paper/2304.00830/) (2023)
- [Active Restoration of Lost Audio Signals Using Machine Learning and Latent Information](https://mlsanity.com/paper/2111.10891/) (2021)
- [Sequence-to-Sequence Multi-Modal Speech In-Painting](https://mlsanity.com/paper/2406.01321/) (2024)
- [BNMusic: Blending Environmental Noises into Personalized Music](https://mlsanity.com/paper/2506.10754/) (2025)

## Abstract (arXiv)

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.