Identifying birdsong syllables without labelled data

[2509.18412] Identifying birdsong syllables without labelled data Skip to main content Search Submit Donate Log in Computer Science > Sound arXiv:2509.18412 (cs) [Submitted on 22 Sep 2025] Title: Identifying birdsong syllables without labelled data Authors: Mélisande Teng , Julien Boussard , David Rolnick , Hugo Larochelle View PDF HTML (experimental) Abstract: Identifying sequences of syllables within birdsongs is key to tackling a wide array of challenges, including bird individual identification and better understanding of animal communication and sensory-motor learning. Recently, machine learning approaches have demonstrated great potential to alleviate the need for experts to label long audio recordings by hand. However, they still typically rely on the availability of labelled data for model training, restricting applicability to a few species and datasets. In this work, we build the first fully unsupervised algorithm to decompose birdsong recordings into sequences of syllables. We first detect syllable events, then cluster them to extract templates -- syllable representations -- before performing matching pursuit to decompose the recording as a sequence of syllables. We evaluate our automatic annotations against human labels on a dataset of Bengalese finch songs and find that our unsupervised method achieves high performance. We also demonstrate that our approach can distinguish individual birds within a species through their unique vocal signatures, for both Bengalese finches and another species, the great tit. Subjects: Sound (cs.SD) ; Machine Learning (cs.LG); Audio and Speech Processing (eess.AS) Cite as: arXiv:2509.18412 [cs.SD] (or arXiv:2509.18412v1 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2509.18412 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1109/ICASSP55912.2026.11464321 Focus to learn more DOI(s) linking to related resources Submission history From: Mélisande Teng [ view email ] [v1] Mon, 22 Sep 2025 20:54:37 UTC (1,729 KB) Full-text links: Access Paper: View PDF HTML (experimental) Source view license Additional Features Audio Summary Current browse context: cs.SD < prev | next > new | recent | 2025-09 Change to browse by: cs cs.LG eess eess.AS References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation 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)

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