SonicMaster：迈向可控的全方位音乐修复与母带处理
SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering
AI总结：
SonicMaster通过基于文本的控制，实现了音乐修复与母带处理的统一生成模型，显著提升了音频质量。
摘要：
音乐录音常常受到音频质量问题的影响，如过度的混响、失真、削波、音量不平衡以及狭窄的立体声图像，尤其是在非专业环境下没有专业设备或专业知识的情况下。这些问题通常需要使用专门的工具和手动调整来纠正。在本文中，我们介绍了SonicMaster，这是首个统一的生成模型，用于音乐修复和母带处理，能够解决广泛音频缺陷问题，通过基于文本的控制。SonicMaster基于自然语言指令进行条件化，以应用定向增强，或者可以自动运行以进行一般修复。为了训练这个模型，我们构建了SonicMaster数据集，一个由配对的降质和高质量曲目组成的大型数据集，通过模拟常见的降质类型，使用十九个属于五个增强组的降质函数：均衡、动态、混响、幅度和立体声。我们的方法利用了流匹配生成训练范式，学习一种音频转换，将降质输入映射到其清洁、母带化的版本，通过文本提示进行指导。客观音频质量指标表明，SonicMaster在所有缺陷类别中显著提高了声音质量。此外，主观听觉测试证实，听众更喜欢SonicMaster增强的输出优于其他基线。
英文摘要：
Music recordings often suffer from audio quality issues such as excessive reverberation, distortion, clipping, tonal imbalances, and a narrowed stereo image, especially when created in non-professional settings without specialized equipment or expertise. These problems are typically corrected using separate specialized tools and manual adjustments. In this paper, we introduce SonicMaster, the first unified generative model for music restoration and mastering that addresses a broad spectrum of audio artifacts with text-based control. SonicMaster is conditioned on natural language instructions to apply targeted enhancements, or can operate in an automatic mode for general restoration. To train this model, we construct the SonicMaster dataset, a large dataset of paired degraded and high-quality tracks by simulating common degradation types with nineteen degradation functions belonging to five enhancements groups: equalization, dynamics, reverb, amplitude, and stereo. Our approach leverages a flow-matching generative training paradigm to learn an audio transformation that maps degraded inputs to their cleaned, mastered versions guided by text prompts. Objective audio quality metrics demonstrate that SonicMaster significantly improves sound quality across all artifact categories. Furthermore, subjective listening tests confirm that listeners prefer SonicMaster's enhanced outputs over other baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
