Underwater acoustic target classification and auditory feature identification based on dissimilarity evaluation

Abstract

The purpose of this study is to explore perceptual classification of underwater acoustic targets and auditory features used by human being. First, we design a paired comparison experiment. Then we use the CLASCAL algorithm to model the dissimilarity ratings as a perceptual space, and analyze the properties in three common dimensions, specialties, 3 subjects' latent classes and their roles in target perceptual classification. Finally, based on the gammatone filterbank, we find some auditory features that can effectively underlie 3 common dimensions and beat properties, so as to use them to construct a binary decision tree to classify some new samples; thus we can provide some guidance about how to use these features in practical applications.