Token-Based Audio Inpainting via Discrete Diffusion
#audio inpainting #discrete diffusion #tokenized representation #pre‑trained audio tokenizer #long gap restoration #semantically coherent reconstruction #machine learning #deep learning #neural networks #audio signal processing
📌 Key Takeaways
- • The preprint (arXiv 2507.08333v4) introduces token‑based discrete diffusion for audio inpainting. • It targets semantically coherent restoration of long missing gaps in music recordings. • The method works on tokenized representations generated by a pre‑trained audio tokenizer. • Two training approaches are integrated to improve learning on partially occluded audio. • Prior diffusion models often fail when the missing region is large, a gap this approach fills.
- • Highlighting the shift from waveform to token space for generative audio models.
- • Combining discrete diffusion with tokenization as a novel strategy for audio repair.
📖 Full Retelling
The authors of the paper "Token-Based Audio Inpainting via Discrete Diffusion" posted a preprint to arXiv (id 2507.08333v4) in July 2025, announcing a new method that restores missing portions of degraded recordings. The study tackles the problem of audio inpainting, which involves filling gaps—especially long ones—in music and audio signals. Researchers developed a technique that applies discrete diffusion processes to tokenized music representations derived from a pre‑trained audio tokenizer, thereby producing stable and semantically coherent reconstructions when previous diffusion‑based methods faltered. The method introduces two training approaches, enabling the model to learn effectively from partially occluded audio data. By moving from continuous waveform manipulation to token‑level diffusion, the approach addresses the limitations of earlier work that struggled with large missing regions.
The paper situates its contribution within the broader context of audio restoration and generative modeling, highlighting why large gaps in audio pose a unique challenge and how token‑based representations can help preserve musical structure and meaning during inpainting.
Conclusion: The study demonstrates that discrete diffusion applied at the token level, coupled with carefully designed training strategies, produces superior inpainting outputs for long missing intervals, marking a significant advance over traditional diffusion‑based audio restoration.
🏷️ Themes
Audio Restoration, Diffusion Models, Tokenization, Generative Modeling, Machine Learning, Music Signal Processing
Entity Intersection Graph
No entity connections available yet for this article.
Original Source
arXiv:2507.08333v4 Announce Type: replace-cross
Abstract: Audio inpainting seeks to restore missing segments in degraded recordings. Previous diffusion-based methods exhibit impaired performance when the missing region is large. We introduce the first approach that applies discrete diffusion over tokenized music representations from a pre-trained audio tokenizer, enabling stable and semantically coherent restoration of long gaps. Our method further incorporates two training approaches: a deriva
Read full article at source