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.
Abstract (translated)
音频修复是指逆向处理数字音频信号中的退化，以恢复在退化发生前的原始高质量信号。这在诸如音乐录音档案的情境中尤其有价值，特别是在历史价值珍贵的情况下，可能已丢失或根本不存在干净版本的原始录音。近期的研究将生成模型应用于音频修复领域，显示出相对于先前方法的重大改进，并开启了能够执行以前无法实现的修复操作的可能性。然而，使这些模型具有精细控制性仍然是一项挑战。 在本文中，我们提出了对FLowHigh的扩展，并引入了动态频谱轮廓（Dynamic Spectral Contour, DSC）作为带宽扩展中的分类器自由引导的控制信号。我们的实验表明该方法具有竞争力的表现，并且DSC作为一个支持细粒度调节的功能显示出前景。
URL
https://arxiv.org/abs/2601.14356
