Classification of acoustic gunshot signatures using a nonparametric Bayesian signal model

Published

Conference Paper

The classification of firearms from their acoustic signatures has many potential benefits for a variety of military and security operations. Most approaches to acoustic gunshot classification can be characterized as frame based feature classification approaches, where the time-domain acoustic signal is partitioned into a set of frames from which characterizing features are extracted and used to classify the signals. Although this approach can be quite successful, performance is highly dependent upon the relationship between the selected frame size and the signals under consideration. In this work we consider a statistical model for time-domain gunshot signatures which eliminates the need for both data partitioning and the selection of characterizing features. Each class of acoustic signals is modeled as a hidden Markov model (HMM) with autoregressive (AR) source densities. Each AR model specifies a set of spectral and energy characteristics of the signal while the HMM characterizes the transitions between these states. The model is constructed using nonparametric Bayesian techniques to allow model inference to learn the number of states within the HMM and the AR order of each state density. The model thus selects the number of unique spectral components and the complexity of each of these components from the set of training data, limiting model over-fitting and eliminating the need to optimize performance over these parameters. We demonstrate that classification using the proposed statistical model performs comparably to existing techniques without requiring user specified features, thus allowing the same statistical models to be used on future datasets without modification. © 2011 SPIE.

Full Text

Duke Authors

Cited Authors

  • Morton, KD; Torrione, PA; Collins, L

Published Date

  • July 13, 2011

Published In

Volume / Issue

  • 8019 /

International Standard Serial Number (ISSN)

  • 0277-786X

International Standard Book Number 13 (ISBN-13)

  • 9780819485939

Digital Object Identifier (DOI)

  • 10.1117/12.885114

Citation Source

  • Scopus