Development 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 and miss many impulse events. In this thesis, 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 selected as inputs to the artificial neural networks. A separate effort attempted to identify military impulse noise by the shape of the recorded waveform. The classification algorithm achieved up to 100% accuracy on the training data and the validation data