Whombat: An open‐source audio annotation tool for machine learning assisted bioacoustics
Open accessAbstract
Automated analysis of bioacoustic recordings using machine learning (ML) methods has the potential to greatly scale biodiversity monitoring efforts. The use of ML for high‐stakes applications, such as conservation and scientific research, demands a data‐centric approach with a focus on selecting and utilizing carefully annotated and curated evaluation and training data that are relevant and representative. Creating annotated bioacoustic datasets presents a number of challenges, such as managing large collections of recordings with associated metadata, developing flexible annotation tools that can accommodate the diverse range of vocalization profiles of different organisms and addressing the scarcity of expert annotators. We present Whombat, a user‐friendly, browser‐based interface for managing audio recordings and annotation projects, with several visualization, exploration and annotation tools. It enables users to quickly annotate, review, and share annotations, as well as visualize and evaluate a set of machine learning predictions on a dataset. The tool facilitates an iterative workflow where user annotations and machine learning predictions feedback to enhance model performance and annotation quality. We demonstrate the flexibility of Whombat by showcasing two distinct use cases: (1) a project aimed at enhancing automated UK bat call identification at the Bat Conservation Trust (BCT), and (2) a collaborative effort among the USDA Forest Service and Oregon State University researchers exploring bioacoustic applications and extending automated avian classification models in the Pacific Northwest, USA. Whombat is a flexible tool that can effectively address the challenges of annotation for bioacoustic research. It can be used for individual and collaborative work, hosted on a shared server or accessed remotely, or run on a personal computer without the need for coding skills. The code is open‐source, and we provide a user guide.
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Cloud-based platforms such as EcoSounds-web and ARBIMON, together with annotation-focused tools like Whombat, enable collaborative annotation, archiving, and querying of large acoustic datasets.
“Cloud-based platforms such as EcoSounds-web and ARBIMON, together with annotation-focused tools like Whombat, enable collaborative annotation, archiving, and querying of large acoustic datasets”
From Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation (2026) · cites this paper as Martínez Balvanera et al. 2025
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Pickering, A., Martinez Balvanera, S., Jones, K. E., & Hedwig, D. (2025) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.70008 · Cited by 5 · see citing works
Self-citationListening forward: emerging roles of bioacoustics in ecology, evolution, and conservation
Zsebők, S. (2026) · Biologia Futura · doi:10.1007/s42977-026-00322-5
“…tools like Whombat, enable collaborative annotation, archiving, and querying of large acoustic datasets (Aide et al. 2013; K. F. Darras et al. 2024; Martínez Balvanera et al. 2025 (Becker et al. 2018; Gupta et al. 2021); autoencoder-based clustering of vocalizations (Bergler et al. 2019);…”
- Authors
- Martínez Balvanera, S., Mac Aodha, O., Weldy, M. J., Pringle, H., Browning, E., & Jones, K. E.
- Affiliations
- Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment University College London London UK
- School of Informatics University of Edinburgh Edinburgh UK
- Oregon State University (US)
- University College London (GB)
- Bat Conservation Trust (GB)
- ORCID
- Venue
- Methods in Ecology and Evolution
- Publisher
- Wiley
- Published
- 2024-12-03
- Volume
- 16
- Issue
- 1
- Pages
- 19-28
- ISSN
- 2041-210X (ISSN-L)
- OpenAlex
- W4405019484
- Cited by
- 11
- References
- 37
- Citations by year
- 2026: 8 · 2025: 3
- FWCI
- 1.97
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Methods in Ecology and Evolution (publishedVersion, cc-by)
- UCL Discovery (University College London) (submittedVersion, cc-by)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Consejo Nacional de Humanidades, Ciencias y Tecnologías (2020‐000017‐02EXTF‐00334)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Developmental Biology › Animal Vocal Communication and Behavior
- Physical Sciences › Environmental Science › Ecology › Marine animal studies overview
- Keywords
- bioacoustics, annotation tools, biodiversity monitoring, annotation quality evaluation, open source software, web interface
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