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The automatic identification of aerospace acoustic sources
This work describes the design of an intelligent recognition system used to distinguish noise
signatures of five different acoustic sources. The system uses pattern recognition techniques to
identify the information obtained from a single microphone. A training phase is used in which the
system learns to distinguish the sources and automatically selects features for optimal performance.
Results were obtained by training the system to distinguish jet planes, propeller planes, a helicopter,
train, and wind turbine from one another, then presenting similar sources to the system and
recording the number of errors. These results indicate the system can successfully identify the
trained sources based on acoustic information. Classification errors highlight the impact of the
training sources on the system's ability to recognize different sources.