@article{ref1,
title="Maximum likelihood decision fusion for weapon classification in wireless acoustic sensor networks",
journal="IEEE/ACM transactions on audio, speech, and language processing",
year="2017",
author="Sánchez-Hevia, H. A. and Ayllón, D. and Gil-Pita, R. and Rosa-Zurera, M.",
volume="25",
number="6",
pages="1172-1182",
abstract="Gunshot acoustic analysis is a field with many practical applications, but due to the multitude of factors involved in the generation of the acoustic signature of firearms, it is not a trivial task. The main problem arises with the strong spatial dependence shown by the recorded waveforms even when dealing with the same weapon. However, this can be lessen by using a spatially diverse receiver such as a wireless acoustic sensor network. In this work, we address multichannel acoustic weapon classification using spatial information and a novel decision fusion rule based on it. We propose a fusion rule based on maximum likelihood estimation that takes advantage of diverse classifier ensembles to improve upon classic decision fusion techniques. Classifier diversity comes from a spatial segmentation that is performed locally at each node. The same segmentation is also used to improve the accuracy of the local classification by means of a divide and conquer approach.<p /> <p>Language: en</p>",
language="en",
issn="2329-9290",
doi="10.1109/TASLP.2017.2690579",
url="http://dx.doi.org/10.1109/TASLP.2017.2690579"
}