将风格迁移模型嵌入音乐制作软件，自动匹配参考曲目的混音参数。
Diff-MSTC: A Mixing Style Transfer Prototype for Cubase
- 用参考歌曲预测混音参数，支持最多20轨音频自动混音。
- 在Cubase中实现端到端混音建议，用户可后续手动微调。
- 首个集成于DAW的深度学习混音风格迁移原型，适合音乐制作人使用。
在演示中，参与者可体验Diff-MSTC原型系统，该系统将Diff-MST模型集成至Steinberg的数字音频工作站（DAW）Cubase中。Diff-MST是一种用于混音风格迁移的深度学习模型，可通过参考歌曲预测轨道的混音台参数。该系统可处理最多20轨原始音频及一个参考歌曲，生成可用于创建初始混音的混音参数。用户可进一步手动调整这些参数以获得更精细的控制。与以往仅限于研究概念的深度学习系统不同，Diff-MSTC是首个集成于DAW中的混音风格迁移原型。该集成使多轨音频的混音决策更便捷，用户可通过参考歌曲输入创作上下文，并沿用传统方式对音效进行微调。
原文摘要 · Abstract (English)
In our demo, participants are invited to explore the Diff-MSTC prototype, which integrates the Diff-MST model into Steinberg's digital audio workstation (DAW), Cubase. Diff-MST, a deep learning model for mixing style transfer, forecasts mixing console parameters for tracks using a reference song. The system processes up to 20 raw tracks along with a reference song to predict mixing console parameters that can be used to create an initial mix. Users have the option to manually adjust these parameters further for greater control. In contrast to earlier deep learning systems that are limited to research ideas, Diff-MSTC is a first-of-its-kind prototype integrated into a DAW. This integration facilitates mixing decisions on multitracks and lets users input context through a reference song, followed by fine-tuning of audio effects in a traditional manner.
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