Authors:
Héctor A. Sánchez-Hevia
;
David Ayllón
;
Roberto Gil-Pita
and
Manuel Rosa-zurera
Affiliation:
University of Alcalá, Spain
Keyword(s):
Gunshot Acoustical Analysis, Pattern Recognition, Divide and Conquer, Feature Extraction.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Audio and Speech Processing
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Classification
;
Digital Signal Processing
;
Feature Selection and Extraction
;
Health Engineering and Technology Applications
;
Multimedia
;
Multimedia Signal Processing
;
Pattern Recognition
;
Signal Processing
;
Software Engineering
;
Telecommunications
;
Theory and Methods
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, especially since the
recorded waveforms show a strong dependence on the shooter’s position and orientation, even when firing the
same weapon. In this paper we address acoustic weapon classification using pattern recognition techniques
with single channel recordings while taking into account the spatial aspect of the problem, so departing from
the typical approach. We are working with three broad categories: rifles, handguns and shotguns. Our approach
is based on two proposals: a Divide and Conquer classification strategy and the inclusion of some
novel features based on the physical model of gunshot acoustics. The Divide and Conquer strategy is aimed
at improving the rate of success of the classification stage by using previously retrieved spatial information to
select between a set of sp
ecialized weapon classifiers. The minimum relative error reduction achieved when
both proposals are used, compared with a single-stage classifier employing traditional features is 38.7%.
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