Generating Separated Singing Vocals Using a Diffusion Model Conditioned on Music Mixtures

• URL: http://arxiv.org/abs/2511.21342v1
• Date: Wed, 26 Nov 2025 12:49:35 GMT
• Title: Generating Separated Singing Vocals Using a Diffusion Model Conditioned on Music Mixtures
• Authors: Genís Plaja-Roglans, Yun-Ning Hung, Xavier Serra, Igor Pereira,
• Abstract summary: In this work, we explore singing voice separation from real music recordings using a diffusion model.<n>We present a study of the sampling algorithm, highlighting the effects of the user-configurable parameters.
• Score: 12.393086516044866
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Separating the individual elements in a musical mixture is an essential process for music analysis and practice. While this is generally addressed using neural networks optimized to mask or transform the time-frequency representation of a mixture to extract the target sources, the flexibility and generalization capabilities of generative diffusion models are giving rise to a novel class of solutions for this complicated task. In this work, we explore singing voice separation from real music recordings using a diffusion model which is trained to generate the solo vocals conditioned on the corresponding mixture. Our approach improves upon prior generative systems and achieves competitive objective scores against non-generative baselines when trained with supplementary data. The iterative nature of diffusion sampling enables the user to control the quality-efficiency trade-off, and also refine the output when needed. We present an ablation study of the sampling algorithm, highlighting the effects of the user-configurable parameters.

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