# GACELA -- A generative adversarial context encoder for long audio inpainting

arXiv: https://arxiv.org/abs/2005.05032 · HTML: https://mlsanity.com/paper/2005.05032/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2020.

## Built on by (most cited first, 15 of 15)

- [AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models](https://mlsanity.com/paper/2304.00830/) (2023)
- [Solving Audio Inverse Problems with a Diffusion Model](https://mlsanity.com/paper/2210.15228/) (2022)
- [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)
- [A2SB: Audio-to-Audio Schrodinger Bridges](https://mlsanity.com/paper/2501.11311/) (2025)
- [The Music Maestro or The Musically Challenged, A Massive Music Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2406.15885/) (2024)
- [Survey on the Evaluation of Generative Models in Music](https://mlsanity.com/paper/2506.05104/) (2025)
- [EdiText: Controllable Coarse-to-Fine Text Editing with Diffusion Language Models](https://mlsanity.com/paper/2502.19765/) (2025)
- [Token-Based Audio Inpainting via Discrete Diffusion](https://mlsanity.com/paper/2507.08333/) (2025)
- [MAIA: An Inpainting-Based Approach for Music Adversarial Attacks](https://mlsanity.com/paper/2509.04980/) (2025)
- [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)
- [Text-Queried Audio Source Separation via Hierarchical Modeling](https://mlsanity.com/paper/2505.21025/) (2025)
- [BNMusic: Blending Environmental Noises into Personalized Music](https://mlsanity.com/paper/2506.10754/) (2025)
- [Multi Codec Discrete Diffusion Model for Text Guided Speech Inpainting and Editing](https://mlsanity.com/paper/2608.06424/) (2026)

## Abstract (arXiv)

We introduce GACELA, a generative adversarial network (GAN) designed to restore missing musical audio data with a duration ranging between hundreds of milliseconds to a few seconds, i.e., to perform long-gap audio inpainting. While previous work either addressed shorter gaps or relied on exemplars by copying available information from other signal parts, GACELA addresses the inpainting of long gaps in two aspects. First, it considers various time scales of audio information by relying on five parallel discriminators with increasing resolution of receptive fields. Second, it is conditioned not only on the available information surrounding the gap, i.e., the context, but also on the latent variable of the conditional GAN. This addresses the inherent multi-modality of audio inpainting at such long gaps and provides the option of user-defined inpainting. GACELA was tested in listening tests on music signals of varying complexity and gap durations ranging from 375~ms to 1500~ms. While our subjects were often able to detect the inpaintings, the severity of the artifacts decreased from unacceptable to mildly disturbing. GACELA represents a framework capable to integrate future improvements such as processing of more auditory-related features or more explicit musical features.