Token-based Audio Inpainting via Discrete Diffusion

Summary

This paper talks about a new method called token-based audio inpainting, which uses a special diffusion model to fill in missing parts of damaged audio recordings by working with tokenized audio data.

What's the problem?

The problem is that when audio recordings have long gaps or missing sections, it’s hard to fix them smoothly and realistically using existing techniques, especially if the missing parts are large.

What's the solution?

The authors created a discrete diffusion model that treats audio as a sequence of tokens, like musical notes or sound chunks, and uses this approach to gradually reconstruct the missing audio sections. This method helps to generate natural-sounding audio that fits well with the original recording.

Why it matters?

This matters because it provides a better way to repair damaged audio, which can be useful in music restoration, podcasts, and other audio applications, improving the listening experience by making broken recordings sound whole again.