Automated robust Anuran classification by extracting elliptical feature pairs from audio spectrograms

Abstract

Ecologists can assess the health of wetlands by monitoring populations of animals such as Anurans (i.e., frogs and toads), which are sensitive to habitat changes. But, surveying anurans requires trained experts to identify species from the animals' mating calls. This identification task can be streamlined by automation. To this end, we propose an automatic frog-call classification algorithm and a smartphone application that drastically simplify the monitoring of anuran populations. We offer three main contributions. First, we introduce a classification method that has an average accuracy of 86% on a dataset of 736 calls from 48 anuran species from the United States. Our dataset is much larger and diverse than those of previous works on anuran classification. Second, we extract a new type of spectrogram feature that avoids syllable segmentation and the manual cleaning of the recordings. Our method also works with recordings of variable length. Third, our method uses GPS location and a voting scheme to reliably deal with a large number of species and high levels of noise.