# Introducing SPAIN (SParse Audio INpainter)

arXiv: https://arxiv.org/abs/1810.13137 · HTML: https://mlsanity.com/paper/1810.13137/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2018.

## Built on by (most cited first, 3 of 3)

- [Correcting Mispronunciations in Speech using Spectrogram Inpainting](https://mlsanity.com/paper/2204.03379/) (2022)
- [Active Restoration of Lost Audio Signals Using Machine Learning and Latent Information](https://mlsanity.com/paper/2111.10891/) (2021)
- [Similarity-Guided Diffusion for Long-Gap Music Inpainting](https://mlsanity.com/paper/2509.16342/) (2025)

## Abstract (arXiv)

A novel sparsity-based algorithm for audio inpainting is proposed. It is an adaptation of the SPADE algorithm by Kiti'c et al., originally developed for audio declipping, to the task of audio inpainting. The new SPAIN (SParse Audio INpainter) comes in synthesis and analysis variants. Experiments show that both A-SPAIN and S-SPAIN outperform other sparsity-based inpainting algorithms. Moreover, A-SPAIN performs on a par with the state-of-the-art method based on linear prediction in terms of the SNR, and, for larger gaps, SPAIN is even slightly better in terms of the PEMO-Q psychoacoustic criterion.