Data from: Towards the automatic classification of avian flight calls for bioacoustic monitoring

Automatic classification of animal vocalizations has great potential to
enhance the monitoring of species movements and behaviors. This is
particularly true for monitoring nocturnal bird migration, where automated
classification of migrants’ flight calls could yield new biological
insights and conservation applications for birds that vocalize during
migration. In this paper we investigate the automatic classification of
bird species from flight calls, and in particular the relationship between
two different problem formulations commonly found in the literature:
classifying a short clip containing one of a fixed set of known species
(N-class problem) and the continuous monitoring problem, the latter of
which is relevant to migration monitoring. We implemented a
state-of-the-art audio classification model based on unsupervised feature
learning and evaluated it on three novel datasets, one for studying the
N-class problem including over 5000 flight calls from 43 different
species, and two realistic datasets for studying the monitoring scenario
comprising hundreds of thousands of audio clips that were compiled by
means of remote acoustic sensors deployed in the field during two
migration seasons. We show that the model achieves high accuracy when
classifying a clip to one of N known species, even for a large number of
species. In contrast, the model does not perform as well in the continuous
monitoring case. Through a detailed error analysis (that included full
expert review of false positives and negatives) we show the model is
confounded by varying background noise conditions and previously unseen
vocalizations. We also show that the model needs to be parameterized and
benchmarked differently for the continuous monitoring scenario. Finally,
we show that despite the reduced performance, given the right conditions
the model can still characterize the migration pattern of a specific
species. The paper concludes with directions for future research.