# Inpainting of long audio segments with similarity graphs

arXiv: https://arxiv.org/abs/1607.06667 · HTML: https://mlsanity.com/paper/1607.06667/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2016.

## Built on by (most cited first, 10 of 10)

- [Arrange, Inpaint, and Refine: Steerable Long-term Music Audio Generation and Editing via Content-based Controls](https://mlsanity.com/paper/2402.09508/) (2024)
- [Catch-A-Waveform: Learning to Generate Audio from a Single Short Example](https://mlsanity.com/paper/2106.06426/) (2021)
- [Token-Based Audio Inpainting via Discrete Diffusion](https://mlsanity.com/paper/2507.08333/) (2025)
- [Is Self-Supervised Learning Enough to Fill in the Gap? A Study on Speech Inpainting](https://mlsanity.com/paper/2405.20101/) (2024)
- [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)
- [Robust Multi-Modal Speech In-Painting: A Sequence-to-Sequence Approach](https://mlsanity.com/paper/2406.00901/) (2024)
- [Sequence-to-Sequence Multi-Modal Speech In-Painting](https://mlsanity.com/paper/2406.01321/) (2024)
- [Similarity-Guided Diffusion for Long-Gap Music Inpainting](https://mlsanity.com/paper/2509.16342/) (2025)
- [Multi Codec Discrete Diffusion Model for Text Guided Speech Inpainting and Editing](https://mlsanity.com/paper/2608.06424/) (2026)

## Abstract (arXiv)

We present a novel method for the compensation of long duration data loss in audio signals, in particular music. The concealment of such signal defects is based on a graph that encodes signal structure in terms of time-persistent spectral similarity. A suitable candidate segment for the substitution of the lost content is proposed by an intuitive optimization scheme and smoothly inserted into the gap, i.e. the lost or distorted signal region. Extensive listening tests show that the proposed algorithm provides highly promising results when applied to a variety of real-world music signals.