An automatic analysis of ultrasound vocalisations for the prediction of interaction context in captive Egyptian fruit bats
<|reference_start|>An automatic analysis of ultrasound vocalisations for the prediction of interaction context in captive Egyptian fruit bats: 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.<|reference_end|>
@article{triantafyllopoulos2024an,
    title={An automatic analysis of ultrasound vocalisations for the prediction of
  interaction context in captive Egyptian fruit bats},
    author={Andreas Triantafyllopoulos, Alexander Gebhard, Manuel Milling, Simon
  Rampp, Bj"orn Schuller},
    journal={arXiv preprint arXiv:2406.06332},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06332},
    primaryClass={cs.SD eess.AS}
}