Quantifying the Effects of Propagation on Classification of Cetacean Vocalizations

In previous work as part of ONR grant N000141210139 a unique automatic classifier developed by the PI that uses perceptual signal features features similar to those employed by the human auditory system was employed to successfully classify anthropogenic transients, and vocalizations from five cetacean species. Although this is a significant achievement, successful implementation of this (or any) classifier requires that it be temporally and spatially robust. The primary goal will be to address the question: Will it work on vocalization data from these species collected under different environmental conditions? To examine this, discriminant analysis will be used to rank the aural features in terms of their ability to separate the vocalizations between species. Then, the more highly ranked features will be tested for robustness. This will be done by performing a propagation experiment using cetacean vocalizations and synthetically generated calls as source signals, and testing the received signals with the classifier. The measurements will be complemented by comparing experimental results to propagation model results with the goal of generalizing the results to other ocean environments.