Passive acoustic monitoring holds great promise for ecological inference, yet existing automated tools are typically narrowly trained and non-transferable. We address these limitations with PULSE, a semi-supervised, multi-task framework for Orthoptera bioacoustics, combining weakly-supervised species classification, self-supervised learning on unlabelled field audio, and knowledge distillation from a general-purpose bioacoustic model. Our domain-adapted specialist model outperforms a state-of-the-art general model across all metrics (macro F1: 0.21 vs. 0.07; AUC: 0.74 vs. 0.45; AP: 0.32 vs. 0.19), with active learning further raising F1 to 0.34 and AUC to 0.84. Beyond classification, the learned embeddings encode ecologically meaningful structure, exposed through an interactive visualisation tool for ecological discovery.

翻译：被动声学监测在生态推断中具有巨大潜力，但现有自动化工具通常训练范围狭窄且不可迁移。我们通过PULSE——一种针对直翅目生物声学的半监督多任务框架——解决了这些局限性，该框架结合了弱监督物种分类、未标注野外音频的自监督学习，以及从通用生物声学模型的知识蒸馏。我们的领域自适应专家模型在所有指标上均优于最先进的通用模型（宏F1：0.21 vs. 0.07；AUC：0.74 vs. 0.45；AP：0.32 vs. 0.19），而主动学习进一步将F1提升至0.34，AUC提升至0.84。除分类外，学习到的嵌入编码了具有生态意义的结构，并通过一个交互式可视化工具为生态发现提供支持。