Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge
Open accessAbstract
Assessing the presence and abundance of birds is important for monitoring specific species as well as overall ecosystem health. Many birds are most readily detected by their sounds, and thus, passive acoustic monitoring is highly appropriate. Yet acoustic monitoring is often held back by practical limitations such as the need for manual configuration, reliance on example sound libraries, low accuracy, low robustness, and limited ability to generalise to novel acoustic conditions. Here, we report outcomes from a collaborative data challenge. We present new acoustic monitoring datasets, summarise the machine learning techniques proposed by challenge teams, conduct detailed performance evaluation, and discuss how such approaches to detection can be integrated into remote monitoring projects. Multiple methods were able to attain performance of around 88% area under the receiver operating characteristic (ROC) curve (AUC), much higher performance than previous general‐purpose methods. With modern machine learning, including deep learning, general‐purpose acoustic bird detection can achieve very high retrieval rates in remote monitoring data, with no manual recalibration, and no pretraining of the detector for the target species or the acoustic conditions in the target environment.
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The inherent complexity of soundscapes requires noise-robust algorithms that generalize well and can separate and identify many animal sounds of interest from confounding natural and anthropogenic signals in a wide variety of acoustic environments.
“Second, the inherent complexity of soundscapes requires noise-robust algorithms that generalize well and can separate and identify many animal sounds of interest from confounding natural and anthropogenic signals in a wide variety of acoustic environments62.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [62]
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- Authors
- Stowell, D., Wood, M. D., Pamuła, H., Stylianou, Y., & Glotin, H.
- Affiliations
- Queen Mary University of London (GB)
- University of Salford (GB)
- AGH University of Krakow (PL)
- University of Crete (GR)
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Stowell, Dan
- Venue
- Methods in Ecology and Evolution
- Publisher
- Wiley
- Published
- 2018-11-03
- Volume
- 10
- Issue
- 3
- Pages
- 368-380
- ISSN
- 2041-210X (ISSN-L)
- OpenAlex
- W2883595988
- Cited by
- 414
- References
- 47
- Citations by year
- 2026: 48 · 2025: 63 · 2024: 48 · 2023: 67 · 2022: 70 · 2021: 58 · 2020: 30 · 2019: 27 · 2018: 3
- FWCI
- 21.03
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Methods in Ecology and Evolution (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- University of Salford Institutional Repository (University of Salford) (submittedVersion, cc-by)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Akademia Górniczo-Hutnicza im. Stanislawa Staszica (15.11.130.642)
- Engineering and Physical Sciences Research Council (EP/L020505/1)
- Natural Environment Research Council (NE/L000520/1)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Developmental Biology › Animal Vocal Communication and Behavior
- Physical Sciences › Environmental Science › Ecology › Avian ecology and behavior
- Keywords
- bird detection, passive acoustic monitoring, deep learning, remote monitoring, species abundance estimation
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