Multi-label classification of frog species via deep learning [post]

Jie Xie
2017 PeerJ Preprints  
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
more » ... nerated 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.
doi:10.7287/peerj.preprints.3007v1 dblp:journals/peerjpre/Xie17 fatcat:pxf26rlcxjfdtl63z62h47jcgq