Cost-sensitive detection with variational autoencoders for environmental acoustic sensing

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...

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Bibliographic Details
Published in:arXiv.org 2017-12
Main Authors: Li, Yunpeng, Kiskin, Ivan, Zilli, Davide, Sinka, Marianne, Chan, Henry, Willis, Kathy, Roberts, Stephen
Format: Article
Language:English
Subjects:
Acoustics
Artificial intelligence
Audio data
Audio signals
Detection
Electronic devices
Embedded systems
Machine learning
Mosquitoes
Parameter sensitivity
Signal processing
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Summary: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.
ISSN:2331-8422