Diff-MSTC: A Mixing Style Transfer Prototype for Cubase

TL;DR

Diff-MSTC is a pioneering deep learning prototype integrated into Cubase that predicts mixing parameters from reference songs, enabling automated initial mixes with user-adjustable controls for professional audio production.

Contribution

This paper introduces the first DAW-integrated deep learning system for mixing style transfer, combining automated parameter prediction with manual fine-tuning.

Findings

Successfully processes up to 20 raw tracks with a reference song

Provides initial mixing parameters that can be manually refined

Demonstrates practical integration of deep learning in professional audio workflows

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.