Passive acoustic monitoring of animal populations with transfer learning
Open accessAbstract
Progress in deep learning, more specifically in using convolutional neural networks (CNNs) for the creation of classification models, has been tremendous in recent years. Within bioacoustics research, there has been a large number of recent studies that use CNNs. Designing CNN architectures from scratch is non-trivial and requires knowledge of machine learning. Furthermore, hyper-parameter tuning associated with CNNs is extremely time consuming and requires expensive hardware. In this paper we assess whether it is possible to build good bioacoustic classifiers by adapting and re-using existing CNNs pre-trained on the ImageNet dataset – instead of designing them from scratch, a strategy known as transfer learning that has proved highly successful in other domains. This study is a first attempt to conduct a large-scale investigation on how transfer learning can be used for passive acoustic monitoring (PAM), to simplify the implementation of CNNs and the design decisions when creating them, and to remove time consuming hyper-parameter tuning phases. We compare 12 modern CNN architectures across 4 passive acoustic datasets that target calls of the Hainan gibbon Nomascus hainanus , the critically endangered black-and-white ruffed lemur Varecia variegata , the vulnerable Thyolo alethe Chamaetylas choloensis , and the Pin-tailed whydah Vidua macroura . We focus our work on data scarcity issues by training PAM binary classification models very small datasets, with as few as 25 verified examples. Our findings reveal that transfer learning can result in up to 82% F1 score while keeping CNN implementation details to a minimum, thus rendering this approach accessible, easier to design, and speeding up further vocalisation annotations to create PAM robust models.
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As few as 20-30 labeled examples may be sufficient to achieve respectable F1 scores, especially when combined with data augmentation and shallow fine-tuning.
“In practice, as few as 20–30 labeled examples may be sufficient to achieve respectable F1 scores—the harmonic mean of precision and recall used to evaluate classification performance—especially when combined with data augmentation and shallow fine-tuning”
From Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation (2026) · cites this paper as Dufourq et al. 2022
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Deep Audio Detection Networks From Passive Acoustic Monitoring of a Critically Endangered Primate
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CNNFET: Convolutional neural network feature Extraction Tools
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The Freshwater Sounds ArchivePreprint
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- Authors
- Dufourq, E., Batist, C., Foquet, R., & Durbach, I.
- Affiliations
- African Institute for Mathematical Sciences (ZA)
- The Graduate Center, CUNY (US)
- University of Cape Town (ZA)
- Corresponding author
- Dufourq, Emmanuel
- Venue
- Ecological Informatics
- Publisher
- Elsevier BV
- Published
- 2022-09
- Volume
- 70
- Pages
- 101688
- ISSN
- 1574-9541 (ISSN-L)
- OpenAlex
- W4282827995
- Cited by
- 97
- References
- 77
- Citations by year
- 2026: 18 · 2025: 27 · 2024: 30 · 2023: 14 · 2022: 8
- FWCI
- 9.10
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Ecological Informatics (publishedVersion, cc-by-nc-nd)
- St Andrews Research Repository (St Andrews Research Repository) (submittedVersion, cc-by-nc-nd)
- SUNScholar (Stellenbosch University) (submittedVersion)
- Funders
- Government of Canada
- International Development Research Centre
- African Institute for Mathematical Sciences
- Global Affairs Canada
- Malawi University of Science and Technology
- Microsoft
- 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 › Marine animal studies overview
- Keywords
- passive acoustic monitoring, transfer learning, convolutional neural networks, bioacoustic classification, Hainan gibbon, black-and-white ruffed lemur, limited training data, wildlife monitoring
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