Disentangling Mixtures of Musical Instruments for Source-level Pitch and Timbre Manipulation

Yin-Jyun Luo

Kin Wai Cheuk

Woosung Choi

Toshimitsu Uesaka

Keisuke Toyama*

Koichi Saito

Chieh-Hsin Lai

Yuhta Takida

Wei-Hsiang Liao

Simon Dixon

Yuki Mitsufuji

* External authors

NeurIPS-24

2025

Abstract

Existing work on pitch and timbre disentanglement has been mostly focused on single-instrument music audio, excluding the cases where multiple instruments are presented. To fill the gap, we propose DisMix, a generative framework in which the pitch and timbre representations act as modular building blocks for constructing the melody and instrument of a source, and the collection of which forms a set of per-instrument latent representations underlying the observed mixture. By manipulating the representations, our model samples mixtures with novel combinations of pitch and timbre of the constituent instruments. We can jointly learn the disentangled pitch-timbre representations and a latent diffusion transformer that reconstructs the mixture conditioned on the set of source-level representations. We evaluate the model using both a simple dataset of isolated chords and a realistic four-part chorales in the style of J.S. Bach, identify the key components for the success of disentanglement, and demonstrate the application of mixture transformation based on source-level attribute manipulation.