ITO-Master：面向音乐母带处理器音频效果建模的推理时优化
ITO-Master: Inference-Time Optimization for Audio Effects Modeling of Music Mastering Processors
AI总结：
本文提出ITO-Master框架，通过在推理时优化参考嵌入实现用户可控的音乐母带风格迁移，实验证明其能提升风格相似性与适应性。
摘要：
音乐母带风格迁移旨在将参考音轨的母带处理特征建模并应用到目标音轨上，以模拟专业的母带制作过程。然而，现有方法基于参考音轨施加固定处理，限制了用户根据艺术意图微调结果的能力。本文提出了ITO-Master框架，这是一个基于参考的母带风格迁移系统，集成了推理时优化（ITO），使用户能够对母带处理过程进行更精细的控制。通过在推理过程中优化参考嵌入，我们的方法允许用户动态地细化输出，进行微观层面的调整，以获得更精确的母带处理结果。我们探索了用于建模母带处理器的黑盒和白盒方法，并证明ITO在不同风格下均能提升母带处理性能。通过客观评估、主观听音测试以及使用CLAP嵌入进行基于文本条件的定性分析，我们验证了ITO在增强母带风格相似性的同时提供了更高的适应性。我们的框架为母带风格迁移提供了一种有效且用户可控的解决方案，使用户能够在初始风格迁移的基础上进一步细化其结果。
英文摘要：
Music mastering style transfer aims to model and apply the mastering characteristics of a reference track to a target track, simulating the professional mastering process. However, existing methods apply fixed processing based on a reference track, limiting users' ability to fine-tune the results to match their artistic intent. In this paper, we introduce the ITO-Master framework, a reference-based mastering style transfer system that integrates Inference-Time Optimization (ITO) to enable finer user control over the mastering process. By optimizing the reference embedding during inference, our approach allows users to refine the output dynamically, making micro-level adjustments to achieve more precise mastering results. We explore both black-box and white-box methods for modeling mastering processors and demonstrate that ITO improves mastering performance across different styles. Through objective evaluation, subjective listening tests, and qualitative analysis using text-based conditioning with CLAP embeddings, we validate that ITO enhances mastering style similarity while offering increased adaptability. Our framework provides an effective and user-controllable solution for mastering style transfer, allowing users to refine their results beyond the initial style transfer.
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。
