We use a model of processing in the human auditory system to develop robust representations of signals. These reduced representations are then presented to a neural network for training and classification.

Empirical studies demonstrate that auditory representations compare favorably to direct frequency (magnitude spectrum) representations with respect to classification performance (i.e. probabilities of detection and false alarm). For this comparison the Receiver Operating Characteristic (ROC) curves are generated from signals derived from the standard transient data set (STDS) distributed by DARPA/ONR.