RT Journal Article SR Electronic T1 Machine Source Localization of Tursiops truncatus Whistle-like Sounds in a Reverberant Aquatic Environment JF bioRxiv FD Cold Spring Harbor Laboratory SP 606673 DO 10.1101/606673 A1 SF Woodward A1 D Reiss A1 MO Magnasco YR 2019 UL http://biorxiv.org/content/early/2019/12/02/606673.abstract AB Most research into bottlenose dolphins’ (Tursiops truncatus’) 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 bottlenose dolphins’ 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 the study of not only non-signature whistles but the study of general acoustic exchanges among socializing dolphins. In this paper, we propose the first machine-learning-based approach to identifying the source locations of semi-stationary, tonal, whistle-like sounds in a highly reverberant space, specifically a half-cylindrical dolphin pool. We deliver time-of-flight and normalized cross-correlation measurements to a random forest model for high-feature-volume classification and feature selection, and subsequently deliver the selected features into linear discriminant analysis, linear and quadratic Support Vector Machine (SVM), and Gaussian process models. In our 14-point setup, we achieve perfect classification accuracy and high (MAD of 0.6557 m, IQR = 0.3395 – 1.5694) regression accuracy with fewer than 10,000 features. The regression models yielded better accuracy than the established Steered-Response (SRP) method when all training data were used, and comparable accuracy – even when interpolating at several meters – in the lateral directions when deprived of training data at testing sites; our methods additionally boast improved computation time and the potential for superior accuracy in all domains with more training data.