Restoring degraded music signals is essential to enhance audio quality for downstream music manipulation. Recent diffusion-based music restoration methods have demonstrated impressive performance, and among them, diffusion posterior sampling (DPS) stands out given its intrinsic properties, making it versatile across various restoration tasks. In this paper, we identify that there are potential issues which will degrade current DPS-based methods' performance and introduce the way to mitigate the issues inspired by diverse diffusion guidance techniques including the RePaint (RP) strategy and the Pseudoinverse-Guided Diffusion Models ($\Pi$GDM). We demonstrate our methods for the vocal declipping and bandwidth extension tasks under various levels of distortion and cutoff frequency, respectively. In both tasks, our methods outperform the current DPS-based music restoration benchmarks. We refer to \url{http://carlosholivan.github.io/demos/audio-restoration-2023.html} for examples of the restored audio samples.

翻译：修复退化的音乐信号对于提升下游音乐处理中的音频质量至关重要。近年来，基于扩散的音乐修复方法展现出令人瞩目的性能，其中扩散后验采样因其固有特性脱颖而出，可灵活适用于多种修复任务。本文识别出当前基于DPS的方法存在潜在性能退化问题，并引入受多种扩散引导技术（包括RePaint策略与伪逆引导扩散模型$\Pi$GDM）启发的缓解方案。我们分别在不同程度失真与截止频率条件下，针对歌声去削波及带宽扩展任务验证了所提方法。在两项任务中，我们的方法均优于当前基于DPS的音乐修复基准。修复音频样本示例可参见\url{http://carlosholivan.github.io/demos/audio-restoration-2023.html}。