Name
Title | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2017 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1712.02488 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunpeng Li | 1 | 0 | 4.06 |
| Ivan Kiskin | 2 | 1 | 3.09 |
| Davide Zilli | 3 | 1 | 2.08 |
| Marianne Sinka | 4 | 0 | 2.03 |
| Henry Chan | 5 | 43 | 7.51 |
| Kathy Willis | 6 | 0 | 0.68 |
| stephen j roberts | 7 | 1244 | 174.70 |