Abstract
Audio inpainting refers to the task of reconstructing missing segments in corrupted audio recordings. While prior approaches-including waveform and spectrogram-based diffusion models-have shown promising results for short gaps, they often degrade in quality when gaps exceed 100 milliseconds (ms). In this work, we introduce a novel inpainting method based on discrete diffusion modeling, which operates over tokenized audio representations produced by a pre-trained audio tokenizer. Our approach models the generative process directly in the discrete latent space, enabling stable and semantically coherent reconstruction of missing audio. We evaluate the method on the MusicNet dataset using both objective and perceptual metrics across gap durations up to 300 ms. We further evaluated our approach on the MTG dataset, extending the gap duration to 500 ms. Experimental results demonstrate that our method achieves competitive or superior performance compared to existing baselines, particularly for longer gaps, offering a robust solution for restoring degraded musical recordings. Audio examples of our proposed method can be found at this https URL
Abstract (translated)
音频修复（audio inpainting）是指重建被损坏的音频记录中缺失的部分。尽管之前的方法，包括基于波形和频谱图的扩散模型，在处理短间隔时表现出了良好的结果，但当缺口超过100毫秒(ms)时，它们的质量通常会下降。在本研究中，我们介绍了一种新的修复方法，该方法基于离散扩散建模，并且是在预训练音频标记器生成的标记化音频表示上进行操作的。我们的方法直接对离散潜在空间中的生成过程进行建模，从而能够实现稳定而语义一致的缺失音频重建。 我们在MusicNet数据集上使用客观和感知指标评估了该方法，在300毫秒(ms)以内的缺口长度下进行了测试。我们还在MTG数据集上进一步验证了我们的方法，并将缺口持续时间延长至500毫秒。实验结果表明，与现有基准相比，我们的方法在较长的缺口情况下达到了竞争力或优越的表现，为修复受损音乐录音提供了稳健的解决方案。 有关我们提出的方法的音频示例，请参见此链接：[https URL]（此处应填写实际的URL）。
URL
https://arxiv.org/abs/2507.08333
