BAYESIAN MILITARY IMPULSE NOISE CLASSIFER

摘要

Civilian noise complaints and damage claims have created the need for stations to monitor military impulse noise. However, the stations currently in service suffer from numerous false positive detections (due to wind noise) of impulse events and often miss many events of interest. To improve the accuracy of military impulse noise monitoring, an algorithm based upon a Bayesian classifier with inputs of conventional and custom acoustic metrics is proposed. To train and evaluate the noise classifier approximately 1,000 waveforms were field collected. The final Bayesian noise classifier used kurtosis and crest factor and, the frequency domain metrics, spectral slope and weighted square error as inputs. The EM algorithm is utilized to fit multi-Gaussian distributions to the different classes of data. In testing the classifier performed to accuracies of up to 99.6%.