Employing wavelet-based texture features in ammunition classification

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NCJ Number
310964
Date Published
May 2017
Abstract

Pattern recognition, a branch of machine learning, involves classification of information in images, sounds, and other digital representations. This paper uses pattern recognition to identify which kind of ammunition was used when a bullet was fired based on a carefully constructed set of gunshot sound recordings. To do this task, we show that texture features obtained from the wavelet transform of a component of the gunshot signal, treated as an image, and quantized in gray levels, are good ammunition discriminators. We test the technique with eight different calibers and achieve a classification rate better than 95%. We also compare the performance of the proposed method with results obtained by standard temporal and spectrographic techniques.

(Publisher abstract provided.)

Grant Number(s)
2014-DN-BX-K034
Agencies
NIJ-Sponsored
Publication Type
Research (Applied/Empirical)
Date Published: May 1, 2017