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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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“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.”
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Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,893
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Fitting Linear Mixed-Effects Models Using lme4
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
Fitting Linear Mixed-Effects Models Using lme4Preprint
Bates, D. M., Maechler, M., Bolker, B. M., & Walker, S. (2014) · arXiv (Cornell University) · doi:10.3929/ethz-b-000105397 · Cited by 9,435
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Research works printed but not linked: 3
- Salamon J, Bello JP. Deep convolutional neural networks and data augmentation for environmental sound classification. arXiv preprint arXiv:160804363. 2016.
- Goeau H, Glotin H, Vellinga W-P, Planque R, Joly A, editors. LifeCLEF Bird Identification Task 2016. The Arrival of Deep Learning. Working Notes of CLEF 2016-Conference and Labs of the Evaluation forum; 2016; Évora, Portugal.
- Walters CL, Browning E, Jones KE. iBats Jersey Review. London, UK: 2016.
Cited by
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Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., Mathis, A., Mathis, M. W., van Langevelde, F., Burghardt, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-27980-y · Cited by 740
Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring
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Christin, S., Hervet, É., & Lecomte, N. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13256 · Cited by 724
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Frick, W. F., Kingston, T., & Flanders, J. R. (2019) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14045 · Cited by 734
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Bianco, M. J., Gerstoft, P., Traer, J., Ozanich, E., Roch, M. A., Gannot, S., & Deledalle, C.-A. (2019) · The Journal of the Acoustical Society of America · doi:10.1121/1.5133944 · Cited by 609
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Høye, T. T., Ärje, J., Bjerge, K., Hansen, O. L. P., Iosifidis, A., Leese, F., Mann, H. M. R., Meissner, K. K., Melvad, C., & Raitoharju, J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2002545117 · Cited by 491
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Besson, M., Alison, J., Bjerge, K., Gorochowski, T. E., Høye, T. T., Jucker, T., Mann, H. M. R., & Clements, C. F. (2022) · Ecology Letters · doi:10.1111/ele.14123 · Cited by 304
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Sun, Z., Sandoval, L., Crystal-Ornelas, R., Mousavi, S. M., Wang, J., Lin, C., Cristea, N., Tong, D., Carande, W. H., Ma, X., et al. (2022) · Computers & Geosciences · doi:10.1016/j.cageo.2022.105034 · Cited by 271
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Borowiec, M. L., Dikow, R. B., Frandsen, P. B., McKeeken, A. J., Valentini, G., & White, A. E. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13901 · Cited by 295
Olival, K. J., Cryan, P. M., Amman, B. R., Baric, R. S., Blehert, D. S., Brook, C. E., Calisher, C. H., Castle, K. T., Coleman, J. T. H., Daszak, P., et al. (2020) · PLoS Pathogens · doi:10.1371/journal.ppat.1008758 · Cited by 172
Mcloughlin, M. P., Stewart, R., & McElligott, A. G. (2019) · Journal of The Royal Society Interface · doi:10.1098/rsif.2019.0225 · Cited by 156
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Gray, P. C., Fleishman, A. B., Klein, D. J., McKown, M., Bézy, V. S., Lohmann, K. J., & Johnston, D. W. (2018) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13132 · Cited by 158
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Parsons, M. J. G., Lin, T.-H., Mooney, T. A., Erbe, C., Juanes, F., Lammers, M., Li, S., Linke, S., Looby, A., Nedelec, S. L., et al. (2022) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2022.810156 · Cited by 121
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Jourjine, N., & Hoekstra, H. E. (2021) · Neuron · doi:10.1016/j.neuron.2021.02.002 · Cited by 118
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Wearn, O. R., Freeman, R., & Jacoby, D. M. P. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0022-7 · Cited by 112
Self-citationTorney, C. J., Lloyd‐Jones, D. J., Chevallier, M., Moyer, D. C., Maliti, H. T., Mwita, M., Kohi, E. M., & Hopcraft, G. C. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13165 · Cited by 110
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Cuan, K., Zhang, T., Huang, J., Fang, C., & Guan, Y. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105688 · Cited by 110
Artificial Intelligence Meets Citizen Science to Supercharge Ecological Monitoring
McClure, E. C., Sievers, M., Brown, C. J., Buelow, C. A., Ditria, E. M., Hayes, M. A., Pearson, R. M., Tulloch, V., Unsworth, R. K. F., & Connolly, R. M. (2020) · Patterns · doi:10.1016/j.patter.2020.100109 · Cited by 124
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Zhong, M., Castellote, M., Dodhia, R. M., Ferres, J. L., Keogh, M. J., & Brewer, A. M. (2020) · The Journal of the Acoustical Society of America · doi:10.1121/10.0000921 · Cited by 103
Pichler, M., & Hartig, F. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13687 · Cited by 108
Alibabaei, K., Gaspar, P. D., Lima, T. M., Campos, R. M., Girão, I., Monteiro, J. B., & Lopes, C. M. (2022) · Remote Sensing · doi:10.3390/rs14030638 · Cited by 107
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Borowicz, A., Le, H., Humphries, G. R. W., Nehls, G., Höschle, C., Kosarev, V., & Lynch, H. J. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212532 · Cited by 96
Lotfian, M., Ingensand, J., & Brovelli, M. A. (2021) · Sustainability · doi:10.3390/su13148087 · Cited by 95
Prince, P., Hill, A. P., Piña‐Covarrubias, E., Doncaster, C. P., Snaddon, J. L., & Rogers, A. (2019) · Sensors · doi:10.3390/s19030553 · Cited by 79
Sharma, S., Sato, K., & Gautam, B. P. (2023) · Sustainability · doi:10.3390/su15097128 · Cited by 79
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Clare, J. D. J., Townsend, P. A., Anhalt‐Depies, C., Locke, C. M., Stenglein, J. L., Frett, S., Martin, K. J., SINGH, A. K., Deelen, T. R. V., & Zuckerberg, B. (2019) · Ecological Applications · doi:10.1002/eap.1849 · Cited by 71
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Ponti, M., & Seredko, A. (2022) · Humanities and Social Sciences Communications · doi:10.1057/s41599-022-01049-z · Cited by 42
Kershenbaum, A., Akçay, Ç., Saheer, L. B., Barnhill, A., Best, P., Cauzinille, J., Clink, D. J., Dassow, A., Dufourq, E., Growcott, J., et al. (2024) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.13155 · Cited by 72
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Bergler, C., Smeele, S. Q., Tyndel, S. A., Barnhill, A., Ortiz, S. T., Kalan, A. K., Cheng, R. X., Brinkløv, S., Osiecka, A. N., Tougaard, J., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-26429-y · Cited by 48
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Xu, P., Arcondoulis, E., & Liu, Y. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.107370 · Cited by 45
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Roemer, C., Julien, J., Ahoudji, P. P., Chassot, J., Genta, M., Colombo, R., Botto, G., Negreira, C., Djossa, B. A., Ing, R. K., et al. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13721 · Cited by 38
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Using tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics
Williams, B., van Merriënboer, B., Dumoulin, V., Hamer, J., Fleishman, A. B., McKown, M., Munger, J. E., Rice, A. N., Lillis, A., White, C., et al. (2025) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2024.0280 · Cited by 32
Self-citationThe Sonozotz project: Assembling an echolocation call library for bats in a megadiverse country
Zamora‐Gutierrez, V., Ortega, J., Flores, R. Á., Aguilar‐Rodríguez, P. A., MONTANO, M. A., Avila‐Torresagatón, L. G., Ayala‐Berdón, J., Bolívar-Cimé, B. D. S., Briones-Salas, M. A., Chan‐Noh, M., et al. (2020) · Ecology and Evolution · doi:10.1002/ece3.6245 · Cited by 32
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Lakdari, M. W., Ahmad, A. H., Sethi, S. S., Bohn, G. A., & Clink, D. J. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102457 · Cited by 32
Balantic, C., & Donovan, T. M. (2019) · Bioacoustics · doi:10.1080/09524622.2019.1605309 · Cited by 28
Kobayashi, K., Masuda, K., Haga, C., Matsui, T., Fukui, D., & Machimura, T. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101253 · Cited by 32
The use of solar farms by bats in mosaic landscapes: Implications for conservation
Szabadi, K. L., Kurali, A., Rahman, N. A. A., Froidevaux, J. S. P., Tinsley, E., Jones, G., Görföl, T., Estók, P., & Zsebők, S. (2023) · Global Ecology and Conservation · doi:10.1016/j.gecco.2023.e02481 · Cited by 28
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Self-citationPhan, T.-T.-H., Nguyen-Doan, D., Nguyen, H. D., Nguyen-Van, H., & Pham-Hong, T. (2022) · Soft Computing · doi:10.1007/s00500-022-07596-6 · Cited by 25
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Morera, A. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102545 · Cited by 23
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Ingram, D. J., Ferreira, G. B., Jones, K. E., & Mace, G. M. (2020) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2020.11.004 · Cited by 27
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Lin, T., Akamatsu, T., & Tsao, Y. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008698 · Cited by 23
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MacIsaac, J., Newson, S. E., Ashton‐Butt, A., Pearce, H., & Milner, B. P. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102851 · Cited by 19
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Krivek, G., Gillert, A., Harder, M., Fritze, M., Frankowski, K., Timm, L., Meyer‐Olbersleben, L., von Lukas, U. F., Kerth, G., & van Schaik, J. (2023) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.339 · Cited by 15
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Self-citation
Metadata
- 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 › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › 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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