Abstract
Civilian noise complaints from urban encroachment have curtailed military exercises that produce high levels of impulse noise. Currently, monitoring stations are in place around many military installations to monitor noise levels. However, these monitoring stations do not provide real-time data and suffer from false positive detections. This project sought to develop algorithms with improved noise classification accuracy. Various types of military impulse noise and nonimpulse noise were measured and processed. Approximately 1000 waveforms were field collected (670 nonimpulse and 330 military impulse). Although several metrics were examined, the conventional time domain metrics of kurtosis and crest factor as well as two novel frequency domain metrics, weighted square error and spectral slope, were found to work best. The computed metric values were used to train and verify the performance of the artificial neural network (ANN), which was able to achieve 100% accuracy on training data and 100% accuracy on verification data. [This research was supported wholly by the U.S. Department of Defense, through the Strategic Environmental Research and Development Program (SERDP).]
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