Bat detective—Deep learning tools for bat acoustic signal detection
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Abstract
Passive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings. We developed a convolutional neural network based open-source pipeline for detecting ultrasonic, full-spectrum, search-phase calls produced by echolocating bats. Our deep learning algorithms were trained on full-spectrum ultrasonic audio collected along road-transects across Europe and labelled by citizen scientists from www.batdetective.org. When compared to other existing algorithms and commercial systems, we show significantly higher detection performance of search-phase echolocation calls with our test sets. As an example application, we ran our detection pipeline on bat monitoring data collected over five years from Jersey (UK), and compared results to a widely-used commercial system. Our detection pipeline can be used for the automatic detection and monitoring of bat populations, and further facilitates their use as indicator species on a large scale. Our proposed pipeline makes only a small number of bat specific design decisions, and with appropriate training data it could be applied to detecting other species in audio. A crucial novelty of our work is showing that with careful, non-trivial, design and implementation considerations, state-of-the-art deep learning methods can be used for accurate and efficient monitoring in audio.
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Detecting and classifying animal species with DL for population estimates is becoming increasingly popular for images, acoustic spectrograms, and videos.
“For these reasons, detecting and classifying animal species with DL for the purpose of population estimates is becoming increasingly popular for images52,53, acoustic spectrograms95, and videos96.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [95]
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- Authors
- Mac Aodha, O., Gibb, R., Barlow, K. E., Browning, E., Firman, M., Freeman, R., Harder, B., Kinsey, L., Mead, G. R., Newson, S. E., Pandourski, I., Parsons, S., Russ, J., Szodoray-Paradi, A., Szodoray-Paradi, F., Tilova, E., Girolami, M., Brostow, G., & Jones, K. E.
- Affiliations
- University College London (GB)
- Bat Conservation Trust (GB)
- Zoological Society of London (GB)
- British Trust for Ornithology (GB)
- Bulgarian Academy of Sciences (BG)
- Queensland University of Technology (AU)
- Imperial College London (GB)
- Corresponding author
- Jones, Kate E.
- ORCID
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-03-08
- Volume
- 14
- Issue
- 3
- Pages
- e1005995
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC5843167
- OpenAlex
- W2922355292
- Cited by
- 216
- References
- 64
- Citations by year
- 2026: 22 · 2025: 27 · 2024: 20 · 2023: 26 · 2022: 35 · 2021: 27 · 2020: 25 · 2019: 26 · 2018: 8
- FWCI
- 18.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Funders
- Engineering and Physical Sciences Research Council (EP/K015664/1, EP/K503745/1)
- Natural Environment Research Council (NE/P016677/1)
- Darwin Initiative (15003, 161333, EIDPR075)
- Philip Leverhulme Prize
- Topics
- Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Bat Biology and Ecology Studies
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Developmental Biology › Animal Vocal Communication and Behavior
- Keywords
- bat echolocation calls, convolutional neural networks, passive acoustic monitoring, bat population monitoring, citizen science, Jersey
- MeSH terms
- Machine Learning, Algorithms, Animals, Chiroptera, Echolocation, Environmental Monitoring, Signal Processing, Computer-Assisted, Zoology, Neural Networks, Computer, Computational Biology, Endangered Species
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