  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.
