The DOI matches its Crossref record; 62 of the 64 references it cites were found in a registry or an index.
Bat detective—Deep learning tools for bat acoustic signal detection
Open accessPublished version of a preprint
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Flagged first, then by citations
2 of the 64 references couldn’t be matched to a known paper.
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
Sorted by citations
Perspectives in machine learning for wildlife conservation
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
Gibb, R., Browning, E., Glover‐Kapfer, P., & Jones, K. E. (2018) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13101 · Cited by 753
Self-citationApplications for deep learning in ecology
Christin, S., Hervet, É., & Lecomte, N. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13256 · Cited by 724
A review of the major threats and challenges to global bat conservation
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
Machine learning in acoustics: Theory and applications
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
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
