Impact of transfer learning methods and dataset characteristics on generalization in birdsong classification
Open accessAbstract
Animal sounds can be recognised automatically by machine learning, and this has an important role to play in biodiversity monitoring. Yet despite increasingly impressive capabilities, bioacoustic species classifiers still exhibit imbalanced performance across species and habitats, especially in complex soundscapes. In this study, we explore the effectiveness of transfer learning in large-scale bird sound classification across various conditions, including single- and multi-label scenarios, and across different model architectures such as CNNs and Transformers. Our experiments demonstrate that both finetuning and knowledge distillation yield strong performance, with cross-distillation proving particularly effective in improving in-domain performance on Xeno-canto data. However, when generalizing to soundscapes, shallow finetuning exhibits superior performance compared to knowledge distillation, highlighting its robustness and constrained nature. Our study further investigates how to use multi-species labels, in cases where these are present but incomplete. We advocate for more comprehensive labeling practices within the animal sound community, including annotating background species and providing temporal details, to enhance the training of robust bird sound classifiers. These findings provide insights into the optimal reuse of pretrained models for advancing automatic bioacoustic recognition.
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Models trained in one acoustic environment may struggle to generalize across different habitats, recording devices, or background soundscapes.
“Models trained in one acoustic environment may also struggle to generalize across different habitats, recording devices, or background soundscapes”
From Listening forward: emerging roles of bioacoustics in ecology, evolution, and conservation (2026) · cites this paper as Ghani et al. 2025
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- Authors
- Ghani, B., Kalkman, V. J., Planqué, B., Vellinga, W.-P., Gill, L., & Stowell, D.
- Affiliations
- Naturalis Biodiversity Center (NL)
- Corresponding author
- Ghani, Burooj
- Venue
- Scientific Reports
- Published
- 2025-05-09
- Volume
- 15
- Issue
- 1
- Pages
- 16273-16273
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC12064815
- OpenAlex
- W4410224488
- Cited by
- 22
- References
- 42
- Citations by year
- 2026: 16 · 2025: 6
- FWCI
- 10.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- The Digital Academic Repository of Naturalis Biodiversity Center (Naturalis Biodiversity Center) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- MAMBO (101060639)
- GUARDEN (101060693)
- 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 › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- transfer learning, knowledge distillation, fine-tuning, multi-label classification, soundscape, biodiversity monitoring, Xeno-canto, pre-trained models, convolutional neural networks, transformer models
- MeSH terms
- Machine Learning, Animals, Birds, Vocalization, Animal
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