Abstract
Music inpainting aims to reconstruct missing segments of a corrupted recording. While diffusion-based generative models improve reconstruction for medium-length gaps, they often struggle to preserve musical plausibility over multi-second gaps. We introduce Similarity-Guided Diffusion Posterior Sampling (SimDPS), a hybrid method that combines diffusion-based inference with similarity search. Candidate segments are first retrieved from a corpus based on contextual similarity, then incorporated into a modified likelihood that guides the diffusion process toward contextually consistent reconstructions. Subjective evaluation on piano music inpainting with 2-s gaps shows that the proposed SimDPS method enhances perceptual plausibility compared to unguided diffusion and frequently outperforms similarity search alone when moderately similar candidates are available. These results demonstrate the potential of a hybrid similarity approach for diffusion-based audio enhancement with long gaps.
Abstract (translated)
音乐补全（Music inpainting）的目标是重建受损录音中缺失的部分。虽然基于扩散的生成模型可以改善中等长度缺口的重构效果，但在处理多秒级缺口时往往难以保持音乐的真实性。我们引入了一种名为“相似性引导的扩散后验采样”（Similarity-Guided Diffusion Posterior Sampling, SimDPS）的混合方法，该方法结合了基于扩散的推理和相似度搜索技术。首先从语料库中根据上下文相似度检索候选片段，然后将这些候选片段整合到一个修改过的似然函数中，以指导扩散过程朝向符合上下文一致性的重建方向。 在钢琴音乐补全实验中，使用2秒长缺口进行主观评估表明：与未引导的扩散方法相比，所提出的SimDPS方法提高了感知真实性；同时，在有适度相似候选片段可用的情况下，该方法通常优于单独使用的相似性搜索。这些结果证明了混合相似度方法在处理长时间间隙时对基于扩散音频增强技术的巨大潜力。
URL
https://arxiv.org/abs/2509.16342
