Authors
- 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
Venue
- NeurIPS-24
Date
- 2025
Disentangling Mixtures of Musical Instruments for Source-level Pitch and Timbre Manipulation
Yin-Jyun Luo
Kin Wai Cheuk
Woosung Choi
Keisuke Toyama*
Koichi Saito
Simon Dixon
* 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.
Related Publications
Consistency Training (CT) has recently emerged as a promising alternative to diffusion models, achieving competitive performance in image generation tasks. However, non-distillation consistency training often suffers from high variance and instability, and analyzing and impr…
Detecting musical versions (different renditions of the same piece) is a challenging task with important applications. Because of the ground truth nature, existing approaches match musical versions at the track level (e.g., whole song). However, most applications require to …
Diffusion models have demonstrated exceptional performances in various fields of generative modeling, but suffer from slow sampling speed due to their iterative nature. While this issue is being addressed in continuous domains, discrete diffusion models face unique challenge…
JOIN US
Shape the Future of AI with Sony AI
We want to hear from those of you who have a strong desire
to shape the future of AI.