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.

翻译：以往的计算生物声学研究主要集中于检测动物在特定栖息地的存在。然而，动物声音所包含的信息远不止存在性；其中还蕴含着这些动物与同种其他个体之间的互动信息。在自然环境中研究这些互动几乎不可能实现，因为通常缺乏真实标注数据。转而利用圈养动物为此提供了一条可行的替代路径。然而，现有研究大多采用传统的基于统计学的方法来分析互动。本研究突破这一标准框架，尝试使用深度神经网络预测圈养埃及果蝠（Rousettus Aegyptiacus）个体间互动的潜在情境。我们实现了超过30%的非加权平均召回率——达到随机水平的三倍以上——并展示了与统计分析不同的误差模式。因此，这项研究标志着通过声音自动分析动物状态的重要进展。