  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.
