Automatic Music Mixing Using a Generative Model of Effect Embeddings

Eloi Moliner

Marco A. Martínez-Ramírez

Junghyun Koo

Wei-Hsiang Liao

Kin Wai Cheuk

Joan Serrà

Vesa Välimäki*

Yuki Mitsufuji

* External authors

ICASSP-26

2026

Abstract

Music mixing involves combining individual tracks into a cohesive mixture, a task characterized by subjectivity where multiple valid solutions exist for the same input. Existing automatic mixing systems treat this task as a deterministic regression problem, thus ignoring this multiplicity of solutions. Here we introduce MEGAMI (Multitrack Embedding Generative Auto MIxing), a generative framework that models the conditional distribution of professional mixes given unprocessed tracks. MEGAMI uses a track-agnostic effects processor conditioned on per-track generated embeddings, handles arbitrary unlabeled tracks through a permutation-equivariant architecture, and enables training on both dry and wet recordings via domain adaptation. Our objective evaluation using distributional metrics shows consistent improvements over existing methods, while listening tests indicate performances approaching human-level quality across diverse musical genres.