Investigation of domain adaptation for acoustic frog species classification
- Published
- Accepted
- Subject Areas
- Artificial Intelligence
- Keywords
- Bioacoustics, Acoustic feature extraction, Domain adaptation
- Copyright
- © 2018 Xie
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. Investigation of domain adaptation for acoustic frog species classification. PeerJ Preprints 6:e26485v1 https://doi.org/10.7287/peerj.preprints.26485v1
Abstract
Acoustic frog species classification has received much attention for its importance in assessing biodiversity. However, most previous frog call classification models are trained and tested using the data collected from the same area, which greatly limits the model's generalization. In practice, frogs often have regional accents. When training and testing data are collected from different areas, there is an adverse impact on frog call classification performance. To tackle this problem, this paper investigates domain adaptation for classifying frog calls collected from different areas. To evaluate the performance of our proposed methods, two frog call datasets, which are collected from subtropical eastern Australia and tropical north-eastern Australia, are used. Experimental results demonstrate that domain adaptation can significantly improve the weighted F1-score from 72.8% to 85.5%.
Author Comment
Submitted to INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE