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.

翻译：被动声学监测(PAM)在生物声学研究中至关重要，它实现了非侵入式物种追踪和生物多样性监测。公民科学平台通过焦点录音（即有意录制目标物种的录音）提供了大量带标注的数据集。然而，PAM需要在被动声景中进行监测，这导致了焦点录音与被动录音之间存在域偏移，对基于焦点录音训练的深度学习模型构成了挑战。为解决域泛化问题，我们利用监督对比学习，通过强制来自不同域的同类样本具有域不变性来实现。此外，我们提出了ProtoCLR，作为SupCon损失函数的一种替代方案，它通过将样本与类别原型进行比较，而非进行两两比较，从而降低了计算复杂度。我们基于大规模鸟声基准数据集BIRB进行了小样本分类实验，以评估预训练的生物声学模型。我们的研究结果表明，ProtoCLR是SupCon的一个更优替代方案。