# Sparsity and cosparsity for audio declipping: a flexible non-convex approach

arXiv: https://arxiv.org/abs/1506.01830 · HTML: https://mlsanity.com/paper/1506.01830/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2015.

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

- [Solving Audio Inverse Problems with a Diffusion Model](https://mlsanity.com/paper/2210.15228/) (2022)
- [Distortion Audio Effects: Learning How to Recover the Clean Signal](https://mlsanity.com/paper/2202.01664/) (2022)
- [Gaussian Flow Bridges for Audio Domain Transfer with Unpaired Data](https://mlsanity.com/paper/2405.19497/) (2024)
- [Active Restoration of Lost Audio Signals Using Machine Learning and Latent Information](https://mlsanity.com/paper/2111.10891/) (2021)

## Abstract (arXiv)

This work investigates the empirical performance of the sparse synthesis versus sparse analysis regularization for the ill-posed inverse problem of audio declipping. We develop a versatile non-convex heuristics which can be readily used with both data models. Based on this algorithm, we report that, in most cases, the two models perform almost similarly in terms of signal enhancement. However, the analysis version is shown to be amenable for real time audio processing, when certain analysis operators are considered. Both versions outperform state-of-the-art methods in the field, especially for the severely saturated signals.