Singer-Informed Vocal Source Separation for Multi-Singer Music Mixtures

Jocelyn Xu; Minje Kim

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arXiv · 2608.14516

Singer-Informed Vocal Source Separation for Multi-Singer Music Mixtures

Jocelyn Xu·Minje Kim

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Abstract

Music source separation systems typically extract a single vocal track and do not distinguish between multiple singers. We study singer-informed vocal source separation for multi-singer mixtures. Our framework introduces a short enrollment recording of a target singer to guide separation through a learned embedding. The singer embedding is incorporated using feature concatenation or feature-wise linear modulation (FiLM), enabling the model to focus on the target singer while suppressing interference. We construct a duet dataset based on DAMP-VSEP with quality filtering and non-overlapping enrollment segments. Experiments on solo and duet settings show that while baseline models perform well for single-singer mixtures, the proposed method improves target-singer extraction in multi-singer cases, increasing target-singer SI-SDR from 0.33 dB to 5.58 dB. Fr\'echet Audio Distance (FAD) further shows improved perceptual quality and better alignment with target audio distributions. Code and checkpoints are available at https://github.com/jocelynxu01/singer-separation-paper.

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BibTeX RIS

Jocelyn Xu, Minje Kim. 2026-08-14. Singer-Informed Vocal Source Separation for Multi-Singer Music Mixtures. https://arxiv.org/abs/2608.14516

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