Dolphin Vocal Repertoire Project

Real-time Dolphin Whistle Detection and Display

The “Dolphin Whistle Detection and Vocalization Display” project is being led by Dr. Heidi Lyn, University of South Alabama, and Peter Sugarman and aims to better understand the whistle repertoire of a group of dolphins located at the Ocean Adventures facility in Gulfport, Mississippi. Our role in this project relates to the software components used in detecting and displaying whistles.  See how we are using accessible analytical software for this project below.  Elizabeth Ferguson and our partner Jennifer Pettis Schallert and Gabi Alongi are involved in the programming elements of this project.

DeepAcoustics is an open-access MATLAB-based software program designed to develop and deploy deep learning networks for bioacoustic data. This tool, touting an intuitive graphical user interface, was modified from the tool DeepSqueak, which was originally developed to detect and classify ultrasonic vocalizations from rodents in a low noise, laboratory setting. DeepAcoustics is used to create and evaluate deep neural networks for whistle detection in a variety of underwater environments. The native classification features of DeepAcoustics are used to categorize detected whistles.

The Seewave and SoundGen packages available in R are used for acoustic data analysis and sound synthesis. OSA integrated these packages into a custom GUI program called "WhistleGen," which is used to  design and a series of artificial whistles that are similar in tonality to those produced by dolphins.  

ARTwarp is a freely available MATLAB-based program that uses whistle contour similarity and an Adaptive Resonance Theory (ART) neural network to categorize tonal sounds.  It was developed by Volker Deecke and Vincent Janik of the University of St Andrews.  ARTwarp provided categorization of whistles which were subsequently analyzed to characterize the group's repertoire.

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