@article{ref1,
title="Classification and modeling of acoustic gunshot signatures",
journal="Arabian journal for science and engineering Part A",
year="2013",
author="Djeddou, Mustapha and Touhami, Tayeb",
volume="38",
number="12",
pages="3399-3406",
abstract="In this paper, we deal with the problem of gunshot classification according to its acoustic signature in a noisy environment. The precise selection of useful signal is essential for a significant features' extraction. For this purpose, we propose to use a preprocessing step to select accurately the gunshot signature related frames. In addition, the use of entire features set with the Gaussian mixture model (GMM) does not provide a high classification rate. Then, a hierarchical classification is proposed. This latter is based on cepstral features modeled by GMM algorithm and followed by a classification of two subclasses using temporal parameter. This contributes significantly in discriminating between close signature classes. Our experiments yielded a high correct classification rate up to 96.29 %. The used temporal parameter is related to muzzle blast signal. To generalize the use of this feature, an empirical equation is derived and validated with a real data set.<p /><p>Language: en</p>",
language="en",
issn="1319-8025",
doi="10.1007/s13369-013-0655-5",
url="http://dx.doi.org/10.1007/s13369-013-0655-5"
}