Identification of bats is most practically done by exploiting the characteristic features of their echolocation calls. This usually involves expert knowledge, expensive equipment and time-consuming post processing of previously recorded calls. Automated solutions exist, but are usually not as accurate as human experts. We present an automated solution for the processing of bat calls and identification of bat species with extremely high classification accuracy that can be used during live recording or in an automated post-processing software. Our algorithm is the first application of a Deep Convolutional Neural Network to classify bat species based on sound spectrogram images of their echolocation calls. We tested several deep CNN architectures including a modified Google Inception and a ResNet50 architecture. The nets were trained on a very large call database consisting of images of snippets of call spectrograms. All our software was developed in the Python programming language and an executable of the software is available on request. [Abstract]
Original Study:
Schwab, E., Pogrebnoj, S., Freund, M., Flossmann, F., Vogl, S., Frommolt K.-H. (2022): Automated bat call classification using deep convolutional neural networks, Bioacoustics. https://doi.org/10.1080/09524622.2022.2050816