Authors
- Carlos Hernandez-Olivan*
- Koichi Saito
- Naoki Murata
- Chieh-Hsin Lai
- Marco A. Martínez-Ramírez
- Wei-Hsiang Liao
- Yuki Mitsufuji
* External authors
Venue
- ICASSP-2024
Date
- 2024
Share
VRDMG: Vocal Restoration via Diffusion Posterior Sampling with Multiple Guidance
Carlos Hernandez-Olivan*
Koichi Saito
Marco A. Martínez-Ramírez
* External authors
ICASSP-2024
2024
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 (ΠGDM). 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{this http URL} for examples of the restored audio samples.
Related Publications
To accelerate sampling, diffusion models (DMs) are often distilled into generators that directly map noise to data in a single step. In this approach, the resolution of the generator is fundamentally limited by that of the teacher DM. To overcome this limitation, we propose …
Generating novel views from a single image remains a challenging task due to the complexity of 3D scenes and the limited diversity in the existing multi-view datasets to train a model on. Recent research combining large-scale text-to-image (T2I) models with monocular depth e…
This paper presents the crossing scheme (X-scheme) for improving the performance of deep neural network (DNN)-based music source separation (MSS) with almost no increasing calculation cost. It consists of three components: (i) multi-domain loss (MDL), (ii) bridging operation…
JOIN US
Shape the Future of AI with Sony AI
We want to hear from those of you who have a strong desire
to shape the future of AI.
