Abstract
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.
Abstract (translated)
在我们的演示中，参与者被邀请探索将Diff-MST模型集成到Steinberg的数字音频工作站（DAW）Cubase中的Diff-MSTC原型。Diff-MST是一种用于混音风格转换的深度学习模型，它使用参考歌曲来预测音轨的调音台参数。该系统能够处理最多20条原始音轨和一首参考歌曲，以预测可用于创建初步混音的调音台参数。用户可以选择进一步手动调整这些参数，以获得更大的控制权。与仅限于研究概念的早期深度学习系统不同，Diff-MSTC是第一个集成到DAW中的此类原型。这种集成有助于在多轨录音中做出混音决策，并允许用户通过输入参考歌曲来提供上下文信息，随后可以按照传统方式进行音频效果的微调。
URL
https://arxiv.org/abs/2411.06576
