Perceptually-driven signal analysis for acoustic event classification

In many acoustic signal processing applications human listeners are able to outperform automated processing techniques, particularly in the identification and classification of acoustic events. The research discussed in this paper develops a framework for employing perceptual information from human listening experiments to improve automatic event classification. We focus on the identification of new signal attributes, or features, that are able to predict the human performance observed in formal listening experiments. Using this framework, our newly identified features have the ability to elevate automatic classification performance closer to the level of human listeners.;We develop several new methods for learning a perceptual feature transform from human similarity measures. In addition to providing a more fundamental basis for uncovering perceptual features than previous approaches, these methods also lead to a greater insight into how humans perceive sounds in a dataset. We also develop a new approach for learning a perceptual distance metric. This metric is shown to be applicable to modern kernel-based techniques used in machine learning and provides a connection between the fields of psychoacoustics and machine learning.;Our research demonstrates these new methods in the area of active sonar signal processing. There is anecdotal evidence within the sonar community that human operators are adept in the task of discriminating between active sonar target and clutter echoes. We confirm this ability in a series of formal listening experiments. With the results of these experiments, we then identify perceptual features and distance metrics using our novel methods. The results show better agreement with human performance than previous approaches. While this work demonstrates these methods using perceptual similarity measures from active sonar data, they are applicable to any similarity measure between signals.