Anuran call classification with deep learning

Abstract

Ecologists can assess the health of flooded habitats or wetlands by studying the variations in the populations of bioindicators such as anurans (i.e., frogs and toads). To monitor anuran populations, ecologists manually identify anuran species from audio recordings. This identification task can be significantly streamlined by the availability of an automated method for anuran identification. Previous promising frog-call identification methods have relied on the extraction of pre-designed features from audio spectrograms such as Mel Coefficients and other filter responses. Instead of using pre-designed features, we propose to allow a deep-learning algorithm to find the features that are most important for classification. In work reported in this paper, we used two deep-learning methods that apply convolutional neural networks (CNN) to anuran classification. Transfer learning was also used. The CNN methods was tested on our dataset of 15 frog species, and produced a classification accuracy up to 77%.