Aural classification of impulsive-source active sonar echoes

Abstract

The goal of this effort is to develop automatic target classification technology for active sonar systems by exploiting knowledge of signal processing methods and human auditory processing. Using impulsive-source active sonar data, formal listening experiments were conducted to determine if and how human subjects can discriminate between sonar target and clutter echoes using aural cues alone. Both trained sonar operators and naive listeners at APL-UW were examined to determine a baseline performance level. This level was found to be well above chance for multiple subjects in both groups, validating the accepted wisdom that there are inherent aural cues separating targets from clutter. In a subsequent experiment, feedback was provided to the naive listeners and classification performance dramatically improved, demonstrating that naive listeners can be trained to a level on par with experts. Using these trained listeners at APL-UW, a multidimensional scaling (MDS) listening experiment was designed and conducted. The results of these experiments and an analysis of the data, particularly its correlation with the physical attributes of target and clutter echoes (i.e., signal features), will be presented.