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

[2608.14516] Singer-Informed Vocal Source Separation for Multi-Singer Music Mixtures Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Audio and Speech Processing arXiv:2608.14516 (eess) [Submitted on 14 Aug 2026] Title: Singer-Informed Vocal Source Separation for Multi-Singer Music Mixtures Authors: Jocelyn Xu , Minje Kim View a PDF of the paper titled Singer-Informed Vocal Source Separation for Multi-Singer Music Mixtures, by Jocelyn Xu and Minje Kim View PDF HTML (experimental) 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. Fréchet Audio Distance (FAD) further shows improved perceptual quality and better alignment with target audio distributions. Code and checkpoints are available at this https URL . Comments: Accepted at IWAENC 2026 Subjects: Audio and Speech Processing (eess.AS) ; Sound (cs.SD) Cite as: arXiv:2608.14516 [eess.AS] (or arXiv:2608.14516v1 [eess.AS] for this version) https://doi.org/10.48550/arXiv.2608.14516 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Jocelyn Xu [ view email ] [v1] Fri, 14 Aug 2026 17:38:16 UTC (678 KB) Full-text links: Access Paper: View a PDF of the paper titled Singer-Informed Vocal Source Separation for Multi-Singer Music Mixtures, by Jocelyn Xu and Minje Kim View PDF HTML (experimental) TeX Source view license Current browse context: eess.AS < prev | next > new | recent | 2026-08 Change to browse by: cs cs.SD eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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