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.

翻译：在本演示中，参与者将探索 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，它能够依据参考歌曲预测音轨的调音台参数。该系统可处理多达 20 条原始音轨及一首参考歌曲，并预测可用于创建初始混音的调音台参数。用户可选择进一步手动调整这些参数以获得更精细的控制。与早期仅限于研究构想的深度学习系统不同，Diff-MSTC 是首款集成至 DAW 中的原型系统。该集成有助于对多轨音频进行混音决策，并允许用户通过参考歌曲输入上下文信息，随后以传统方式对音频效果进行微调。