Performance of artificial neural network-based classifiers to identify military impulse noise

Noise monitoring stations are in place around some military installations to provide records that assist in processing noise complaints and damage claims. However, they are known to produce false positives (by incorrectly attributing naturally occurring noise to military operations) and also fail to detect many impulse events. In this project, classifiers based on artificial neural networks were developed to improve the accuracy of military impulse noise identification. Two time-domain metrics--kurtosis and crest factor--and two custom frequency-domain metrics--spectral slope and weighted square error-were inputs to the artificial neural networks. The classification algorithm was able to achieve up to 100% accuracy on the training data and the validation data, while improving detection threshold by at least 40 dB.