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arXiv · 2610.17821

FLOMSS: Generative Source Separation by Flow Matching

Shuyang Cui·Zhi Zhong·Keisuke Toyama·Koichi Saito·Qiyu Wu·Yukara Ikemiya·Junghyun Koo·Kin Wai Cheuk·Woosung Choi·Giorgio Fabbro·Wei-Hsiang Liao·Shusuke Takahashi·Yuki Mitsufuji
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Abstract

Music source separation (MSS) has been conventionally addressed by discriminative methods. Recently, flow matching has emerged as a strong generative paradigm for modeling complex data distributions, with successful applications across diverse domains. These advances motivate us to systematically investigate flow matching for MSS in a four-source (Vocals, Bass, Drums, Other) setting in full-band stereo. Specifically, we investigate prediction targets, flow paths, single-source versus multi-source modeling, and mixture consistency constraints. Beyond the signal-level metrics commonly used for MSS, such as signal-to-distortion ratio, we further evaluate MSS methods using embedding-based metrics, as well as a listening test, providing a comprehensive benchmark. On MUSDB18-HQ, our proposed model exhibits competitive performance in both objective and subjective tests, highlighting the potential of flow matching for generative MSS. Demo: https://ik4sumii.github.io/FLOMSS/

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Shuyang Cui, Zhi Zhong, Keisuke Toyama, Koichi Saito, Qiyu Wu, Yukara Ikemiya, Junghyun Koo, Kin Wai Cheuk, Woosung Choi, Giorgio Fabbro, Wei-Hsiang Liao, Shusuke Takahashi, Yuki Mitsufuji. 2026-10-13. FLOMSS: Generative Source Separation by Flow Matching. https://arxiv.org/abs/2610.17821

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