Abstract
A classification system is presented for discriminating different infrasound events using a Parallel Neural Network Classifier Bank (PNNCB) consisting of Radial Basis Function (RBF) networks. The classifier architecture and the pre-processing steps are unique and yield results that are superior when compared with those previously reported. Three-dimensional Receiver Operating Characteristic (ROC) curves are used to optimally set the output thresholds at each of the classification modules in the PNNCB for a particular class. A process is presented that enables optimising certain parameters of the classifier system. An application of the classification system to four infrasound classes is presented along with performance results and associated Confidence Intervals (CIs).
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