Classification of Egyptian Fruit Bat Calls with Deep Learning Methods

Abstract

Bats are of great importance for the survival of all living beings and for biodiversity. This study aims to classify the collective calls of the Egyptian fruit bat, whose northernmost distribution is in Turkey, using deep learning methods CNN and LSTM and utilizing MFCC (Mel Frequency Cepstral Coefficients) features. Thanks to the classification of species-specific calls, it is possible to observe the habitat preference, social relations, foraging, reproduction, mobility and migration of the species. The classification results obtained in this study provide significant increases compared to the previous study, especially in distinguishing certain calls.