Students Research Presentations: Automated identification of bottlenose dolphin (Tursiops truncatus) individuals using signature whistles

Year:

2022

Source:

Ocean Sciences Meeting, Virtual

Abstract:

Passive acoustic monitoring (PAM) provides an opportunity to study the population ecology of vocalizing species, such as bottlenose dolphins (Tursiops truncatus), but as technology advances, the greatest challenge researchers face is developing efficient methods for processing large volumes of PAM data. The creation of an automatic identification algorithm using animal calls, such as dolphin whistles, is a solution to the costly and impractical manual processing of large data volumes. One type of whistle produced by bottlenose dolphins, signature whistles, are a resource for the development of an auto-ID algorithm because they contain identifying information about the dolphins that are present. We created a deep convolutional neural network model capable of recognizing the signature whistles of bottlenose dolphins within PAM data. We developed an algorithm to automate the processing of PAM data including loading, windowing, and Fourier transformation. This study indicates a deep learning network as a promising method of signature whistle identification. Our future work will focus on improving the performance of the method and adapting it for monitoring dolphin presence and abundance. The auto-ID algorithm will enhance our understanding of bottlenose dolphin spatio-temporal distribution, migration, and social structure, and help to inform effective management and conservation policies of this protected species.

Mentors:

Vyacheslav Lyubchich, Ph.D.

Students:

Emma Wilkinson, Flagler College