转移学习方法和数据集特征对鸟歌分类中的概括的影响
Burooj Ghani1, Vincent J Kalkman2, Bob Planqué3
1Naturalis Biodiversity Center, Leiden, The Netherlands. burooj.ghani@naturalis.nl.
Scientific reports
|May 9, 2025
概括
转移学习显著改善了用于生物多样性监测的鸟类声音分类. 浅微调在现实世界声景中表现出色,而更好的数据标签对于强大的生物声学模型至关重要.
科学领域:
- 机器学习在生态学的应用.
- 生物声学和生物多样性监测
背景情况:
- 使用机器学习自动识别物种对于生物多样性监测至关重要.
- 目前的生物声学分类器在与物种和息地之间的性能失衡作斗争,特别是在复杂的声景中.
研究的目的:
- 评估用于大规模鸟类声音分类的转移学习策略.
- 在各种场景中比较不同模型架构 (CNN,变压器) 的有效性,并转移学习技术 (微调,知识蒸).
主要方法:
- 实验涉及使用卷积神经网络 (CNN) 和变压器进行单个和多标签分类任务.
- 转移学习方法,包括微调和知识蒸 (交叉蒸),应用于大型鸟类声音数据集,如Xeno-canto.
- 模型性能在域内评估,并在将其推广到复杂的音景时进行评估.
主要成果:
- 微调和知识蒸都表现出了强的表现.
- 交叉蒸特别提高了Xeno-canto数据的域内性能.
- 浅微调显示,与知识蒸相比,对声音景观的概括性更好,表明更强大的稳定性.
结论:
- 转移学习,特别是浅微调,对于鸟类声音的分类和对复杂环境的概括是有效的.
- 改进数据注释实践,包括背景物种和时间信息,对于开发更强大的生物声学分类器至关重要.
- 这些发现为利用预训练模型来推进自动生物声学识别提供了指导.
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