# A survey and an extensive evaluation of popular audio declipping methods

arXiv: https://arxiv.org/abs/2007.07663 · HTML: https://mlsanity.com/paper/2007.07663/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2020.

## Built on by (most cited first, 7 of 7)

- [Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations](https://mlsanity.com/paper/2303.01664/) (2023)
- [SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering](https://mlsanity.com/paper/2508.03448/) (2025)
- [Equivariance-based self-supervised learning for audio signal recovery from clipped measurements](https://mlsanity.com/paper/2409.15283/) (2024)
- [Distortion Audio Effects: Learning How to Recover the Clean Signal](https://mlsanity.com/paper/2202.01664/) (2022)
- [Is Self-Supervised Learning Enough to Fill in the Gap? A Study on Speech Inpainting](https://mlsanity.com/paper/2405.20101/) (2024)
- [Bottleneck Transformer-Based Approach for Improved Automatic STOI Score Prediction](https://mlsanity.com/paper/2602.15484/) (2026)
- [Learning to reconstruct from saturated data: audio declipping and high-dynamic range imaging](https://mlsanity.com/paper/2602.22279/) (2026)

## Abstract (arXiv)

Dynamic range limitations in signal processing often lead to clipping, or saturation, in signals. The task of audio declipping is estimating the original audio signal, given its clipped measurements, and has attracted much interest in recent years. Audio declipping algorithms often make assumptions about the underlying signal, such as sparsity or low-rankness, and about the measurement system. In this paper, we provide an extensive review of audio declipping algorithms proposed in the literature. For each algorithm, we present assumptions that are made about the audio signal, the modeling domain, and the optimization algorithm. Furthermore, we provide an extensive numerical evaluation of popular declipping algorithms, on real audio data. We evaluate each algorithm in terms of the Signal-to-Distortion Ratio, and also using perceptual metrics of sound quality. The article is accompanied by a repository containing the evaluated methods.