Martínez Balvanera, S;
Mac Aodha, O;
Weldy, MJ;
Pringle, H;
Browning, E;
Jones, KE;
(2024)
Whombat: An open-source audio annotation tool for machine learning assisted bioacoustics.
Methods in Ecology and Evolution
10.1111/2041-210X.14468.
(In press).
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Abstract
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.
Type: | Article |
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Title: | Whombat: An open-source audio annotation tool for machine learning assisted bioacoustics |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1111/2041-210X.14468 |
Publisher version: | https://doi.org/10.1111/2041-210x.14468 |
Language: | English |
Additional information: | Copyright © 2024 The Author(s). Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | AI, audio annotation, bioacoustics, bioinformatics, machine learning, software, sound event detection, visualisation |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Genetics, Evolution and Environment |
URI: | https://discovery.ucl.ac.uk/id/eprint/10202760 |
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