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。