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Cost-sensitive detection with variational autoencoders for environmental acoustic sensing
Yunpeng Li · Stephen J Roberts

Fri Dec 08 05:10 PM -- 05:30 PM (PST) @
Event URL: http://media.aau.dk/smc/wp-content/uploads/2017/12/ML4AudioNIPS17_paper_23.pdf »

(+ Ivan Kiskin, Davide Zilli, Marianne Sinka, Henry Chan, Kathy Willis) 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 project1 which aims to detect the presence of mosquitoes using sound collected by simple embedded devices.

Author Information

Yunpeng Li (University of Oxford)
Stephen J Roberts (University of Oxford)

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