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 fine-tuning 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 fine-tuning 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.

本研究解决了生物声学物种分类器在不同物种和栖息地中的性能不均衡问题，采用了多种迁移学习方法和数据集特征进行大规模鸟音分类。研究发现，细调和知识蒸馏都能显著提高分类性能，而在复杂声景中，浅层细调表现更佳，凸显了其鲁棒性和局限性。建议动物声音领域采用更全面的标注实践，以增强鸟鸣分类器的训练效果。

鸟鸣分类中的泛化：迁移学习方法与数据集特征的影响