Impact of transfer learning methods and dataset characteristics on generalization in birdsong classification
Burooj Ghani; Vincent J. Kalkman; Bob Planqué; Willem-Pier Vellinga; Lisa Gill; Dan Stowell · 2025 · Scientific Reports
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
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
Abstract by Burooj Ghani; Vincent J. Kalkman; Bob Planqué; Willem-Pier Vellinga; Lisa Gill; Dan Stowell, Scientific Reports (2025) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1038/s41598-025-00996-2