面向圈养埃及果蝠交互情境预测的超声发声自动分析
An automatic analysis of ultrasound vocalisations for the prediction of interaction context in captive Egyptian fruit bats
AI总结：
本文提出利用深度神经网络自动分析圈养埃及果蝠的超声发声，以预测其交互情境，未加权平均召回率超过30%，显著优于随机水平，推动了从声音自动分析动物状态的研究。
摘要：
以往的计算生物声学研究大多聚焦于检测特定栖息地中动物的存在。然而，动物声音所包含的信息远比单纯的存在更为丰富；其中还蕴含着这些动物与同物种其他成员之间的交互信息。在自然环境中研究这些交互几乎是不可能的，因为往往缺乏真实标注。使用圈养动物则提供了一条可行的替代途径。然而，大多数先前的工作遵循传统的、基于统计的方法来分析交互。在本工作中，我们超越了这一标准框架，尝试利用深度神经网络预测圈养埃及果蝠（Rousettus Aegyptiacus）交互中的潜在情境。我们取得了超过30%的未加权平均召回率——是随机水平的三倍以上——并展示了与我们的统计分析不同的错误模式。因此，本工作代表了朝着从声音自动分析动物状态迈出的重要一步。
英文摘要：
Prior work in computational bioacoustics has mostly focused on the detection of animal presence in a particular habitat. However, animal sounds contain much richer information than mere presence; among others, they encapsulate the interactions of those animals with other members of their species. Studying these interactions is almost impossible in a naturalistic setting, as the ground truth is often lacking. The use of animals in captivity instead offers a viable alternative pathway. However, most prior works follow a traditional, statistics-based approach to analysing interactions. In the present work, we go beyond this standard framework by attempting to predict the underlying context in interactions between captive \emph{Rousettus Aegyptiacus} using deep neural networks. We reach an unweighted average recall of over 30\% -- more than thrice the chance level -- and show error patterns that differ from our statistical analysis. This work thus represents an important step towards the automatic analysis of states in animals from sound.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
