Paper
Statistical Machine Learning Algorithms for Target Classification from Acoustic Signature
Machine learning classification algorithms are relevant to a large number of Army classification prob-lems, including the determination of a weapon class from a detonation acoustic signature. However, much such work has been focused on classification of events from small weapons used for asymmet-ric warfare, which have been of importance in recent years. In this work we consider classification of very different weapon classes, such as mortar, rockets and RPGs, which are difficult to reliably classify with standard techniques since they tend to have similar acoustic signatures. To address this problem, we compare two recently-introduced state-of-the-art machine learning algorithms, Support Vector Machines and Discriminative Restricted Boltzmann Machines, and develop how to use them to solve this difficult acoustic classification task. We obtain classification accuracy results that could make these techniques suitable for fielding on autonomous devices. Discriminative Restricted Boltzmann Machines appear to yield slightly better accuracy than Support Vector Machines, and are less sensitive to the choice of signal preprocessing and model hyperparameters. Importantly, we also address methodological issues that one faces in order to rigorously compare several classifiers on limited data collected from field trials; these questions are of significance to any application of machine learning methods to Army problems. Approved for public release; distribution is unlimited 1 1
Authors: Vincent Mirelli · S.M. Tenney · Yoshua Bengio · Nicolas Chapados · Olivier Delalleau