  Automated classification of animal vocalisations is a potentially powerful
wildlife monitoring tool. Training robust classifiers requires sizable
annotated datasets, which are not easily recorded in the wild. To circumvent
this problem, we recorded four primate species under semi-natural conditions in
a wildlife sanctuary in Cameroon with the objective to train a classifier
capable of detecting species in the wild. Here, we introduce the collected
dataset, describe our approach and initial results of classifier development.
To increase the efficiency of the annotation process, we condensed the
recordings with an energy/change based automatic vocalisation detection.
Segmenting the annotated chunks into training, validation and test sets,
initial results reveal up to 82% unweighted average recall (UAR) test set
performance in four-class primate species classification.
