This paper presents a novel approach to audio restoration, focusing on the enhancement of low-quality music recordings, and in particular historical ones. Building upon a previous algorithm called BABE, or Blind Audio Bandwidth Extension, we introduce BABE-2, which presents a series of improvements. This research broadens the concept of bandwidth extension to \emph{generative equalization}, a novel task that, to the best of our knowledge, has not been explicitly addressed in previous studies. BABE-2 is built around an optimization algorithm utilizing priors from diffusion models, which are trained or fine-tuned using a curated set of high-quality music tracks. The algorithm simultaneously performs two critical tasks: estimation of the filter degradation magnitude response and hallucination of the restored audio. The proposed method is objectively evaluated on historical piano recordings, showing an enhancement over the prior version. The method yields similarly impressive results in rejuvenating the works of renowned vocalists Enrico Caruso and Nellie Melba. This research represents an advancement in the practical restoration of historical music.

翻译：本文提出了一种新颖的音频修复方法，专注于提升低质量音乐录音（尤其是历史录音）的音质。基于先前称为BABE（盲音频带宽扩展）的算法，我们引入了BABE-2，该算法呈现了一系列改进。本研究将带宽扩展的概念拓展至**生成式均衡**这一新任务——据我们所知，该任务在以往研究中尚未被明确探讨。BABE-2的核心是采用扩散模型先验的优化算法，这些模型通过精选的高质量音乐曲目进行训练或微调。该算法同步执行两个关键任务：滤波器退化幅度响应的估计与修复音频的幻觉生成。通过对历史钢琴录音的客观评估，所提方法显示出对先前版本的性能提升。该方法在修复著名歌唱家恩里科·卡鲁索和内莉·梅尔巴的作品时同样取得了令人瞩目的效果。本研究标志着历史音乐实际修复技术的重要进展。