Detonation Classification from Acoustic Signature with the Restricted Boltzmann Machine

We compare the recently proposed Discriminative Restricted Boltzmann Machine to the classical Support Vector Machine on a challenging classification task consisting in identifying weapon classes from audio signals. The three weapon classes considered in this work (mortar, rocket and rocket-propelled grenade), are difficult to reliably classify with standard techniques since they tend to have similar acoustic signatures. In addition, specificities of the data available in this study makes it challenging to rigorously compare classifiers, and we address methodological issues arising from this situation. Experiments show good classification accuracy that could make these techniques suitable for fielding on autonomous devices. Discriminative Restricted Boltzmann Machines appear to yield better accuracy than Support Vector Machines, and are less sensitive to the choice of signal preprocessing and model hyperparameters. This last property is especially appealing in such a task where the lack of data makes model validation difficult.