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Journal Article

Citation

Sánchez-Hevia HA, Ayllón D, Gil-Pita R, Rosa-Zurera M. IEEE/ACM Trans. Audio Speech Lang. Process. 2017; 25(6): 1172-1182.

Copyright

(Copyright © 2017, Institute of Electrical and Electronics Engineers)

DOI

10.1109/TASLP.2017.2690579

PMID

unavailable

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.


Language: en

Keywords

Feature extraction; Weapons; acoustic communication (telecommunication); acoustic signal processing; Acoustics; classifier diversity; Communication system security; Decision fusion; decision fusion rule; decision fusion technique; divide-conquer approach; firearm acoustic signature; gunshot acoustic analysis; gunshots; maximum likelihood decision fusion; maximum likelihood estimation; Microphones; multi-channel classification; multichannel acoustic weapon classification; sensor fusion; signal classification; spatial dependence; spatial information; spatial segmentation; spatially-diverse receiver; weapons; wireless acoustic sensor networks; Wireless communication; wireless sensor networks; Wireless sensor networks

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