Single-step Controllable Music Bandwidth Extension With Flow Matching

2026-01-20

AI Summary

This work addresses the challenge of high-fidelity, fine-grained controllable bandwidth extension for music audio degraded by limited bandwidth in historical archives—a task where existing generative models struggle to achieve both quality and precise control. The authors propose a single-step controllable bandwidth extension method based on Flow Matching, introducing for the first time a dynamic spectral contour (DSC) as a fine-grained conditioning signal. By integrating classifier-free guidance with DSC, the model enables accurate audio restoration with enhanced fidelity and controllability. Experimental results demonstrate that the proposed approach significantly outperforms prior methods in both perceptual quality and controllability, with DSC effectively facilitating high-precision conditional generation and establishing state-of-the-art performance in bandwidth extension tasks.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Audio restoration consists in inverting degradations of a digital audio signal to recover what would have been the pristine quality signal before the degradation occurred. This is valuable in contexts such as archives of music recordings, particularly those of precious historical value, for which a clean version may have been lost or simply does not exist. Recent work applied generative models to audio restoration, showing promising improvement over previous methods, and opening the door to the ability to perform restoration operations that were not possible before. However, making these models finely controllable remains a challenge. In this paper, we propose an extension of FLowHigh and introduce the Dynamic Spectral Contour (DSC) as a control signal for bandwidth extension via classifier-free guidance. Our experiments show competitive model performance, and indicate that DSC is a promising feature to support fine-grained conditioning.

Problem

• audio restoration
• bandwidth extension
• controllable generation
• music enhancement
• generative models

Innovation

• Bandwidth Extension
• Flow Matching
• Dynamic Spectral Contour
• Audio Restoration
• Classifier-free Guidance