Cost-sensitive detection with variational autoencoders for environmental acoustic sensing

TL;DR

This paper introduces a cost-sensitive classification method using variational autoencoders within a Neyman-Pearson framework to improve environmental acoustic sensing, specifically for mosquito detection with embedded devices.

Contribution

It presents a novel cost-sensitive classification approach that integrates variational autoencoders and Neyman-Pearson principles for better control of false positive and false negative rates.

Findings

Effective control of false positive/negative trade-off

Improved mosquito detection accuracy

Applicable to embedded acoustic sensing devices

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.