发表机构
Naturalis Biodiversity Center; Department of Cognitive Science and Artificial Intelligence, Tilburg University; People and Nature Lab, University College London; Leiden Institute of Advanced Computer Science, Leiden University（自然生物多样性中心; 蒂尔堡大学认知科学与人工智能系; 伦敦大学学院人与自然实验室; 莱顿大学莱顿高级计算机科学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ChiroEcho框架联合预测蝙蝠的属与物种，结合地理分布将欧洲蝙蝠自动分类覆盖从73%提升至85%，为解决未见过的细粒度类别问题提供了原理验证。
AI 中文摘要
蝙蝠是生态系统健康的关键指示物种，且在整个欧洲受到保护，因此可靠的种群监测是保护工作的优先事项。它们隐秘的夜行性生活方式使得被动声学监测必不可少，但自动识别仍然困难，因为回声定位叫声会随行为和环境变化，且不同物种的叫声存在重叠。我们提出一种深度学习框架，该框架可联合预测物种和属，并在推理时将属预测与地理物种分布相结合。当预测属中仅有一种物种出现在某区域时，该框架可识别学习分类体系之外的物种。这将地理信息重新定义为扩展而非限制分类器有效分类体系的手段。我们使用涵盖35种欧洲蝙蝠物种的录音，评估闭集分类、检验稀疏代表物种的性能估计不稳定性，并开展受控的留作验证的原理验证实验。稀有物种分析显示，有限的评估数据会掩盖物种级性能；留作验证的实验表明，属预测和位置信息可恢复物种分支无法获取的标签。地理分辨率将操作覆盖范围从48种欧洲原生蝙蝠中的35种扩展至41种，覆盖比例从73%提升至85%。据我们所知，这是已报道的欧洲蝙蝠自动分类中最广的操作覆盖范围。更广泛而言，该蝙蝠框架为通过结合粗粒度预测与透明外部约束来解决未见过的细粒度类别问题提供了原理验证。
英文摘要
Bats are key indicators of ecosystem health and are protected throughout Europe, making reliable population monitoring a conservation priority. Their cryptic nocturnal lifestyle makes passive acoustic monitoring essential, yet automated identification remains difficult as echolocation calls vary with behaviour and environment and overlap among species. We present a deep learning framework that jointly predicts species and genus and combines genus predictions with geographic species distributions at inference. When only one species of a predicted genus occurs in a region, the framework can resolve species absent from the learned taxonomy. This reframes geographic information as a means of extending, rather than constraining, a classifier's effective taxonomy. Using recordings spanning 35 European bat species, we evaluate closed-set classification, examine the instability of performance estimates for sparsely represented species, and conduct a controlled held-out proof-of-principle experiment. The rare-species analysis shows how limited evaluation data can obscure species-level performance, while the held-out experiment shows that genus predictions and location can recover labels unavailable to the species head. Geographic resolution extends operational coverage from 35 to 41 of the 48 native European bat species, increasing coverage from 73% to 85%. To our knowledge, this is the broadest operational coverage reported for automated European bat classification. More broadly, the bat framework provides proof of principle for resolving unseen fine-grained classes by combining coarse predictions with transparent external constraints.
Comments24 pages, 3 figures. Accepted at the CV4E workshop, ECCV 2026
