An automatic analysis of ultrasound vocalisations for the prediction of interaction context in captive Egyptian fruit bats

TL;DR

This paper develops a deep learning approach to analyze ultrasound vocalizations of captive Egyptian fruit bats, enabling the prediction of interaction contexts with significantly improved accuracy over chance, advancing automatic animal state analysis.

Contribution

It introduces a deep neural network method for predicting interaction contexts from bat vocalizations, surpassing traditional statistical analysis in accuracy.

Findings

Achieved over 30% unweighted average recall, three times chance level.

Deep neural networks reveal error patterns different from statistical methods.

Demonstrates potential for automatic analysis of animal states from sound.

Abstract

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.