animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics

[2406.01253] animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Sound arXiv:2406.01253 (cs) [Submitted on 3 Jun 2024 ( v1 ), last revised 14 Apr 2026 (this version, v3)] Title: animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics Authors: Julian C. Schäfer-Zimmermann , Vlad Demartsev , Baptiste Averly , Kiran Dhanjal-Adams , Mathieu Duteil , Gabriella Gall , Marius Faiß , Lily Johnson-Ulrich , Dan Stowell , Marta B. Manser , Marie A. Roch , Ariana Strandburg-Peshkin View a PDF of the paper titled animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics, by Julian C. Sch\"afer-Zimmermann and 10 other authors View PDF Abstract: Bioacoustic research, vital for understanding animal behavior, conservation, and ecology, faces a monumental challenge: analyzing vast datasets where animal vocalizations are rare. While deep learning techniques are becoming standard, adapting them to bioacoustics remains difficult. We address this with animal2vec, an interpretable large transformer model, and a self-supervised training scheme tailored for sparse and unbalanced bioacoustic data. It learns from unlabeled audio and then refines its understanding with labeled data. Furthermore, we introduce and publicly release MeerKAT: Meerkat Kalahari Audio Transcripts, a dataset of meerkat (Suricata suricatta) vocalizations with millisecond-resolution annotations, the largest labeled dataset on non-human terrestrial mammals currently available. Our model outperforms existing methods on MeerKAT and the publicly available NIPS4Bplus birdsong dataset. Moreover, animal2vec performs well even with limited labeled data (few-shot learning). animal2vec and MeerKAT provide a new reference point for bioacoustic research, enabling scientists to analyze large amounts of data even with scarce ground truth information. Comments: Code available at: this https URL | Dataset available at: this https URL Subjects: Sound (cs.SD) ; Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS); Quantitative Methods (q-bio.QM); Applications (stat.AP) Cite as: arXiv:2406.01253 [cs.SD] (or arXiv:2406.01253v3 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2406.01253 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Methods in Ecology and Evolution, 17, 875 888 (2026) Related DOI : https://doi.org/10.1111/2041-210x.70218 Focus to learn more DOI(s) linking to related resources Submission history From: Julian Schäfer-Zimmermann PhD [ view email ] [v1] Mon, 3 Jun 2024 12:11:01 UTC (8,458 KB) [v2] Fri, 26 Jul 2024 07:39:30 UTC (9,345 KB) [v3] Tue, 14 Apr 2026 08:49:24 UTC (1,696 KB) Full-text links: Access Paper: View a PDF of the paper titled animal2vec and MeerKAT: A self-supervised transformer for rare-event raw audio input and a large-scale reference dataset for bioacoustics, by Julian C. Sch\"afer-Zimmermann and 10 other authors View PDF view license Current browse context: cs.SD < prev | next > new | recent | 2024-06 Change to browse by: cs cs.AI eess eess.AS q-bio q-bio.QM stat stat.AP References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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