Sparse Distributed Associative Memory for the Identification of Aerospace Acoustic Sources

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