Multi-label classification of frog species via deep learning
- Published
- Accepted
- Subject Areas
- Artificial Intelligence, Data Mining and Machine Learning, Multimedia
- Keywords
- Frog call classification, Multi-label learning, Deep learning, Soundscape ecology, Time-frequency representation
- Copyright
- © 2017 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
- 2017. Multi-label classification of frog species via deep learning. PeerJ Preprints 5:e3007v1 https://doi.org/10.7287/peerj.preprints.3007v1
Abstract
Acoustic classification of frogs has received increasing attention for its promising application in ecological studies. Various studies have been proposed for classifying frog species, but most recordings are assumed to have only a single species. In this study, a method to classify multiple frog species in an audio clip is presented. To be specific, continuous frog recordings are first cropped into audio clips (10 seconds). Then, various time-frequency representations are generated for each 10-s recording. Next, instead of using traditional hand-crafted features, a deep learning algorithm is used to find the most important feature. Finally, a binary relevance based multi-label classification approach is proposed to classify simultaneously vocalizing frog species with our proposed features. Experimental results show that our proposed features extracted using deep learning can achieve better classification performance when compared to hand-crafted features for frog call classification.
Author Comment
This is a preprint submission to PeerJ Preprints.