VRDMG: VOCAL RESTORATION VIA DIFFUSION POSTERIOR SAMPLING
WITH MULTIPLE GUIDANCE

Carlos Hernandez-Olivan^{*, †}, Koichi Saito^†, Naoki Murata^†, Chieh-Hsin Lai^†, Marco A. Rammirez-Martinez^†, Wei-Hsing Liao^†, Yuki Mitsufuji^†^†

^*Dept. of Electronic Engineering and Communications, University of Zaragoza, Spain
^†Sony AI, Tokyo, Japan
^†Sony Group Corporation, Tokyo, Japan

Abstract

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 http://carlosholivan.github.io/demos/audio-restoration-2023.html for examples of the restored audio samples.