Performance of a neural network based transient classifier at monitoring an acoustic perimeter intruder detection system

Abstract

An investigation was carried out to evaluate the performance of a Multi-Layer Perceptron based neural network transient classifier for detecting attacks, using bolt cutters, on security fences. A tape containing acoustic recordings from fence mounted microphonic cable security systems was used in the investigation. The data was digitised and Fourier Transformed and the resulting spectrograms were subject to detailed examination, in conjunction with aural analysis, in order to deduce appropriate time/frequency resolution for distinguishing genuine attacks from background signals. This facilitated the selection of suitable candidate sets of processing parameters for the system. The data was then partitioned into training and test data. Normalised spectrograms were extracted from the training data and labelled appropriately as "Fencecut" or "Backgrnd" for use as training templates for the neural networks. A back-propagation algorithm was used for training the neural networks.