提出ProtoCLR提升鸟类声音模型跨场景泛化能力
Domain-Invariant Representation Learning of Bird Sounds
- 用类别原型替代成对比较，降低对比学习计算开销
- 在BIRB数据集上实现少样本分类，验证方法有效性
- 适合做生物声学监测与跨域音频模型研究者
被动声学监测（PAM）对生物声学研究至关重要，可实现非侵入式物种追踪与生物多样性监测。公民科学平台提供大量聚焦录音的标注数据集，但实际PAM需在被动声景中运行，导致聚焦录音与被动录音之间存在领域差异，使基于聚焦录音训练的深度学习模型面临挑战。为提升模型的领域泛化能力，本文采用监督对比学习，强制同一类样本在不同领域间保持不变性。此外，提出ProtoCLR，通过将样本与类别原型进行对比而非成对比较，降低计算复杂度。我们在大规模鸟类声音基准数据集BIRB上开展少样本分类实验，评估预训练生物声学模型性能。结果表明，ProtoCLR是SupCon的更优替代方案。
原文摘要 · Abstract (English)
Passive acoustic monitoring (PAM) is crucial for bioacoustic research, enabling non-invasive species tracking and biodiversity monitoring. Citizen science platforms provide large annotated datasets from focal recordings, where the target species is intentionally recorded. However, PAM requires monitoring in passive soundscapes, creating a domain shift between focal and passive recordings, challenging deep learning models trained on focal recordings. To address domain generalization, we leverage supervised contrastive learning by enforcing domain invariance across same-class examples from different domains. Additionally, we propose ProtoCLR, an alternative to SupCon loss which reduces the computational complexity by comparing examples to class prototypes instead of pairwise comparisons. We conduct few-shot classification based on BIRB, a large-scale bird sound benchmark to assess pre-trained bioacoustic models. Our findings suggest that ProtoCLR is a better alternative to SupCon.
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