SPARSE DISTRIBUTED ASSOCIATIVE MEMORY FOR THE IDENTIFICATION OF AEROSPACE ACOUSTIC SOURCES

A pattern recognition system has been developed to classify five diffe rent serospace acoustic sources. The system consists of one microphone for data acquisition, a preprocessor, a feature selector, and a class ifier. In this paper the performances of an associative memory classif ier and a neural network classifier are compared with the performance of a previously designed system. Source noises are classified using fe atures calculated from the time and frequency domain. Each classifier is trained to classify source noises correctly using a set of known so urces. After training, the classifier is tested with unknown sources. Results show that over 96% of the sources were identified correctly wi th the new associative memory classifier. The neural network classifie r identified over 81% of the sources correctly.