  Environmental acoustic sensing involves the retrieval and processing of audio
signals to better understand our surroundings. While large-scale acoustic data
make manual analysis infeasible, they provide a suitable playground for machine
learning approaches. Most existing machine learning techniques developed for
environmental acoustic sensing do not provide flexible control of the trade-off
between the false positive rate and the false negative rate. This paper
presents a cost-sensitive classification paradigm, in which the
hyper-parameters of classifiers and the structure of variational autoencoders
are selected in a principled Neyman-Pearson framework. We examine the
performance of the proposed approach using a dataset from the HumBug project
which aims to detect the presence of mosquitoes using sound collected by simple
embedded devices.
