Machine Source Localization of Tursiops truncatus Whistle-like Sounds in a Reverberant Aquatic Environment

Most research into bottlenose dolphins9 (Tursiops truncatus9) capacity for communication has centered on tonal calls termed whistles, in particular individually-distinctive contact calls referred to as signature whistles. While "non-signature" whistles exist, and may be important components of the bottlenose dolphins9 communicative repertoire, they have not been studied extensively. This is in part due to the difficulty of attributing whistles to specific individuals, a challenge that has limited not only the study of non-signature whistles but the study of general acoustic exchanges between socializing dolphins. In this paper, we propose the first machine-learning-based approach to identifying the source locations of tonal, whistle-like sounds in a highly reverberant space, specifically a half-cylindrical dolphin pool. We feed time-of-flight and normalized cross-correlation measurements into a random forest model for high-feature-volume classification and feature selection, and subsequently feed the selected features into linear discriminant analysis, linear and quadratic SVM, and Gaussian process models. In our 14-point setup, we achieve perfect classification accuracy and high (3.22 +/- 2.63 feet) regression accuracy with less than 10,000 features, suggesting an upgrade in accuracy and computational efficiency to the whole-pool-sampling SRP-PHAT method that is the only competitive alternative at present.