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Bat detective—Deep learning tools for bat acoustic signal detection

Author

Listed:
  • Oisin Mac Aodha
  • Rory Gibb
  • Kate E Barlow
  • Ella Browning
  • Michael Firman
  • Robin Freeman
  • Briana Harder
  • Libby Kinsey
  • Gary R Mead
  • Stuart E Newson
  • Ivan Pandourski
  • Stuart Parsons
  • Jon Russ
  • Abigel Szodoray-Paradi
  • Farkas Szodoray-Paradi
  • Elena Tilova
  • Mark Girolami
  • Gabriel Brostow
  • Kate E Jones

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.Author summary: There is a critical need for robust and accurate tools to scale up biodiversity monitoring and to manage the impact of anthropogenic change. For example, the monitoring of bat species and their population dynamics can act as an important indicator of ecosystem health as they are particularly sensitive to habitat conversion and climate change. In this work we propose a fully automatic and efficient method for detecting bat echolocation calls in noisy audio recordings. We show that our approach is more accurate compared to existing algorithms and other commercial tools. Our method enables us to automatically estimate bat activity from multi-year, large-scale, audio monitoring programmes.

Suggested Citation

  • Oisin Mac Aodha & Rory Gibb & Kate E Barlow & Ella Browning & Michael Firman & Robin Freeman & Briana Harder & Libby Kinsey & Gary R Mead & Stuart E Newson & Ivan Pandourski & Stuart Parsons & Jon Rus, 2018. "Bat detective—Deep learning tools for bat acoustic signal detection," PLOS Computational Biology, Public Library of Science, vol. 14(3), pages 1-19, March.
  • Handle: RePEc:plo:pcbi00:1005995
    DOI: 10.1371/journal.pcbi.1005995
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    References listed on IDEAS

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    1. Bradley J. Cardinale & J. Emmett Duffy & Andrew Gonzalez & David U. Hooper & Charles Perrings & Patrick Venail & Anita Narwani & Georgina M. Mace & David Tilman & David A. Wardle & Ann P. Kinzig & Gre, 2012. "Biodiversity loss and its impact on humanity," Nature, Nature, vol. 486(7401), pages 59-67, June.
    2. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
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    Cited by:

    1. Devis Tuia & Benjamin Kellenberger & Sara Beery & Blair R. Costelloe & Silvia Zuffi & Benjamin Risse & Alexander Mathis & Mackenzie W. Mathis & Frank Langevelde & Tilo Burghardt & Roland Kays & Holger, 2022. "Perspectives in machine learning for wildlife conservation," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Sandhya Sharma & Kazuhiko Sato & Bishnu Prasad Gautam, 2023. "A Methodological Literature Review of Acoustic Wildlife Monitoring Using Artificial Intelligence Tools and Techniques," Sustainability, MDPI, vol. 15(9), pages 1-20, April.
    3. Maryam Lotfian & Jens Ingensand & Maria Antonia Brovelli, 2021. "The Partnership of Citizen Science and Machine Learning: Benefits, Risks, and Future Challenges for Engagement, Data Collection, and Data Quality," Sustainability, MDPI, vol. 13(14), pages 1-19, July.

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