🤖 AI Summary
Existing automatic music mixing approaches struggle to simultaneously achieve high-quality output and flexible style control. This work proposes Diff2Mix, the first end-to-end mixing system that integrates diffusion-based generative modeling with a differentiable audio mixer. The method enables global mix style guidance through reference audio while allowing users to explicitly adjust audio effect parameters, thereby offering a highly controllable mixing process. Experimental results demonstrate that Diff2Mix achieves state-of-the-art performance in both objective metrics and subjective listening tests, effectively balancing audio quality with editing flexibility.
📝 Abstract
Automatic music mixing aims to combine multitrack recordings into a balanced and coherent musical piece. Because the content of different songs and the subjective preferences of mixing engineers jointly shape the final outcome, a practical system should deliver well-balanced mixes while allowing for controllable stylistic variation. However, most existing methods treat automatic mixing and mixing style control as separate tasks, making it difficult for a single system to produce high-quality mixes while remaining editable and style-aware. To address this limitation, this paper presents Diff2Mix, a generative automatic mixing system based on diffusion models and a differentiable mixing console. This system offers two levels of optional user control: a reference audio enables overall production style control, and the differentiable mixing console provides explicit audio effects parameters for interpretability and fine-grained optimization. We demonstrate our system's competitive performance through both objective and subjective evaluations in terms of mixing quality and control ability. We provide code and audio samples at our project page https://zys711.github.io/Diff2Mix .