Machine learning has the potential to revolutionize passive acoustic
monitoring (PAM) for ecological assessments. However, high annotation and
compute costs limit the field's efficacy. Generalizable pretrained networks can
overcome these costs, but high-quality pretraining requires vast annotated
libraries, limiting its current applicability primarily to bird taxa. Here, we
identify the optimum pretraining strategy for a data-deficient domain using
coral reef bioacoustics. We assemble ReefSet, a large annotated library of reef
sounds, though modest compared to bird libraries at 2% of the sample count.
Through testing few-shot transfer learning performance, we observe that
pretraining on bird audio provides notably superior generalizability compared
to pretraining on ReefSet or unrelated audio alone. However, our key findings
show that cross-domain mixing which leverages bird, reef and unrelated audio
during pretraining maximizes reef generalizability. SurfPerch, our pretrained
network, provides a strong foundation for automated analysis of marine PAM data
with minimal annotation and compute costs.

机器学习对于生态评估中的被动声学监测具有革命性的潜力，通过预训练网络可以降低高昂的注释和计算成本，本研究在珊瑚礁生物声学领域确定了最佳的预训练策略，通过交叉领域混合预训练，实现了用于海洋被动声学监测数据的自动化分析。

利用热带珊瑚礁、鸟类和无关声音进行优秀的海洋生物声学转移学习

Leveraging tropical reef, bird and unrelated sounds for superior  transfer learning in marine bioacoustics

In this work, we propose to use out-of-distribution samples, i.e., unlabeled
samples coming from outside the target classes, to improve few-shot learning.
Specifically, we exploit the easily available out-of-distribution samples to
drive the classifier to avoid irrelevant features by maximizing the distance
from prototypes to out-of-distribution samples while minimizing that of
in-distribution samples (i.e., support, query data). Our approach is simple to
implement, agnostic to feature extractors, lightweight without any additional
cost for pre-training, and applicable to both inductive and transductive
settings. Extensive experiments on various standard benchmarks demonstrate that
the proposed method consistently improves the performance of pretrained
networks with different architectures.

利用来自目标分类之外的未标记样本来改进少样本学习，从而通过最大化样本距离，实现驱动分类器避免不相关特征，有效提升有不同结构的预训练网络的性能。