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arXiv · 1712.02488

Cost-sensitive detection with variational autoencoders for environmental acoustic sensing

Yunpeng Li·Ivan Kiskin·Davide Zilli·Marianne Sinka·Henry Chan·Kathy Willis·Stephen Roberts
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Abstract

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.

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Yunpeng Li, Ivan Kiskin, Davide Zilli, Marianne Sinka, Henry Chan, Kathy Willis, Stephen Roberts. 2017-12-07. Cost-sensitive detection with variational autoencoders for environmental acoustic sensing. https://arxiv.org/abs/1712.02488

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