Cost-sensitive detection with variational autoencoders for environmental acoustic sensing
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.
Cite
@article{arxiv.1712.02488,
title = {Cost-sensitive detection with variational autoencoders for environmental acoustic sensing},
author = {Yunpeng Li and Ivan Kiskin and Davide Zilli and Marianne Sinka and Henry Chan and Kathy Willis and Stephen Roberts},
journal= {arXiv preprint arXiv:1712.02488},
year = {2017}
}
Comments
Presented at the NIPS 2017 Workshop on Machine Learning for Audio Signal Processing