Robust real-time detection of right whale upcalls using neural networks on the edge
Animals worldwide are facing ecological pressures from global climate change and increasing anthropogenic activities. To transition to a renewable energy future, extensive offshore wind development is planned globally. In the North Atlantic, future development sites overlap with the migratory range...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-11-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125001396 |
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| author | Matthew D. Hyer Austin T. Anderson David A. Mann T. Aran Mooney Nadège Aoki Frants H. Jensen |
| author_facet | Matthew D. Hyer Austin T. Anderson David A. Mann T. Aran Mooney Nadège Aoki Frants H. Jensen |
| author_sort | Matthew D. Hyer |
| collection | DOAJ |
| description | Animals worldwide are facing ecological pressures from global climate change and increasing anthropogenic activities. To transition to a renewable energy future, extensive offshore wind development is planned globally. In the North Atlantic, future development sites overlap with the migratory range of critically endangered North Atlantic right whales (NARW) and will lead to increased risk of ship strikes, pile driving impacts, and other population risks. New methods to accurately detect cetaceans and provide real-time feedback for mitigation will be increasingly important to enact sustainable management actions to facilitate the recovery of the NARW. Recent developments in acoustic event detection made possible by deep learning have shown significantly improved detection performance across many different taxa, but such models tend to be too computationally expensive to run on existing wildlife monitoring platforms. Here, we use model compression techniques combined with an autonomous acoustic recording platform integrating an ESP32 microcontroller to bring real-time detection with deep learning to the edge. We test if edge-based inference using a compressed network running on a microprocessor entails significant performance loss and find that this loss is negligible. We leverage large, open-source datasets of noise from the NOAA SanctSound project for generating semi-synthetic training datasets that encourage model generalization to novel noise conditions. Our compressed model achieves improved performance across all tested recording sites in the Western North Atlantic Ocean, demonstrating that deep learning powered wildlife monitoring solutions can provide reliable real-time data for mitigation of human impacts and help ensure a sustainable green energy transition. |
| format | Article |
| id | doaj-art-0dd44bfb36fc49e8aeb16a3d18458c29 |
| institution | DOAJ |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-0dd44bfb36fc49e8aeb16a3d18458c292025-08-20T03:19:56ZengElsevierEcological Informatics1574-95412025-11-018910313010.1016/j.ecoinf.2025.103130Robust real-time detection of right whale upcalls using neural networks on the edgeMatthew D. Hyer0Austin T. Anderson1David A. Mann2T. Aran Mooney3Nadège Aoki4Frants H. Jensen5Department of Ecoscience, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark; Corresponding author.Loggerhead Instruments, 6576 Palmer Park Circle, Sarasota, FL, 34238, USALoggerhead Instruments, 6576 Palmer Park Circle, Sarasota, FL, 34238, USABiology Department, Woods Hole Oceanographic Institution, 266 Woods Hole Road, Woods Hole, MA, 02543, USABiology Department, Woods Hole Oceanographic Institution, 266 Woods Hole Road, Woods Hole, MA, 02543, USA; Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 55-101, Cambridge, MA, 02139, USADepartment of Ecoscience, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark; Biology Department, Woods Hole Oceanographic Institution, 266 Woods Hole Road, Woods Hole, MA, 02543, USA; Department of Biology, Syracuse University, 107 College Pl, Syracuse, NY, 13244, USAAnimals worldwide are facing ecological pressures from global climate change and increasing anthropogenic activities. To transition to a renewable energy future, extensive offshore wind development is planned globally. In the North Atlantic, future development sites overlap with the migratory range of critically endangered North Atlantic right whales (NARW) and will lead to increased risk of ship strikes, pile driving impacts, and other population risks. New methods to accurately detect cetaceans and provide real-time feedback for mitigation will be increasingly important to enact sustainable management actions to facilitate the recovery of the NARW. Recent developments in acoustic event detection made possible by deep learning have shown significantly improved detection performance across many different taxa, but such models tend to be too computationally expensive to run on existing wildlife monitoring platforms. Here, we use model compression techniques combined with an autonomous acoustic recording platform integrating an ESP32 microcontroller to bring real-time detection with deep learning to the edge. We test if edge-based inference using a compressed network running on a microprocessor entails significant performance loss and find that this loss is negligible. We leverage large, open-source datasets of noise from the NOAA SanctSound project for generating semi-synthetic training datasets that encourage model generalization to novel noise conditions. Our compressed model achieves improved performance across all tested recording sites in the Western North Atlantic Ocean, demonstrating that deep learning powered wildlife monitoring solutions can provide reliable real-time data for mitigation of human impacts and help ensure a sustainable green energy transition.http://www.sciencedirect.com/science/article/pii/S1574954125001396Data augmentationDeep learningNorth Atlantic right whaleReal-time acoustic detectionSustainable marine developmentWildlife monitoring |
| spellingShingle | Matthew D. Hyer Austin T. Anderson David A. Mann T. Aran Mooney Nadège Aoki Frants H. Jensen Robust real-time detection of right whale upcalls using neural networks on the edge Ecological Informatics Data augmentation Deep learning North Atlantic right whale Real-time acoustic detection Sustainable marine development Wildlife monitoring |
| title | Robust real-time detection of right whale upcalls using neural networks on the edge |
| title_full | Robust real-time detection of right whale upcalls using neural networks on the edge |
| title_fullStr | Robust real-time detection of right whale upcalls using neural networks on the edge |
| title_full_unstemmed | Robust real-time detection of right whale upcalls using neural networks on the edge |
| title_short | Robust real-time detection of right whale upcalls using neural networks on the edge |
| title_sort | robust real time detection of right whale upcalls using neural networks on the edge |
| topic | Data augmentation Deep learning North Atlantic right whale Real-time acoustic detection Sustainable marine development Wildlife monitoring |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125001396 |
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