Perceptual Feature Identification for Active Sonar Echoes

Abstract

This paper presents a novel method of using psychoacoustic information from human listening experiments to generate useful features for automated signal classification or regression. The design and analysis of a similarity experiment using active sonar transient echoes is summarized and two methods are presented for feature identification based on the results of the listening experiment. These methods not only identify novel features but also provide a visual insight into perceptually significant signal attributes. The approach presented is based on perceptual similarity measures collected during formal listening experiments but is applicable to any perceptual similarity experiment (e.g. visual)