Diff-MST：可微混音风格迁移
Diff-MST: Differentiable Mixing Style Transfer
AI总结：
针对现有混音风格迁移系统轨数固定、缺乏可解释性等问题，提出含可微混音台、transformer控制器和定制损失的Diff-MST框架，支持任意轨数输入与事后调整，性能优于强基线。
摘要：
混音风格迁移通过从参考歌曲中推断制作属性，自动为给定的一组音轨生成多轨混音。然而，现有的混音风格迁移系统存在局限性：它们通常仅能处理固定数量的音轨，会引入伪影，且以端到端方式生成混音，未基于传统音频效果，导致缺乏可解释性与可控性。为克服这些挑战，我们提出Diff-MST框架，该框架由可微混音台、transformer控制器和音频制作风格损失函数组成。通过输入原始音轨与参考歌曲，我们的模型可估计可微混音台内音频效果的控制参数，生成高质量混音并支持事后调整。此外，我们的架构无需源标签即可支持任意数量的输入音轨，可应用于实际场景。我们将模型性能与强基线进行对比评估，验证了所提方法、架构设计、定制化音频制作风格损失以及创新训练方法在该任务上的有效性。
英文摘要：
Mixing style transfer automates the generation of a multitrack mix for a given set of tracks by inferring production attributes from a reference song. However, existing systems for mixing style transfer are limited in that they often operate only on a fixed number of tracks, introduce artifacts, and produce mixes in an end-to-end fashion, without grounding in traditional audio effects, prohibiting interpretability and controllability. To overcome these challenges, we introduce Diff-MST, a framework comprising a differentiable mixing console, a transformer controller, and an audio production style loss function. By inputting raw tracks and a reference song, our model estimates control parameters for audio effects within a differentiable mixing console, producing high-quality mixes and enabling post-hoc adjustments. Moreover, our architecture supports an arbitrary number of input tracks without source labelling, enabling real-world applications. We evaluate our model's performance against robust baselines and showcase the effectiveness of our approach, architectural design, tailored audio production style loss, and innovative training methodology for the given task.
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。
