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Neural network classification of gunshots using spectral characteristics

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dc.title Neural network classification of gunshots using spectral characteristics en
dc.contributor.author Navrátil, Milan
dc.contributor.author Křesálek, Vojtěch
dc.contributor.author Dostálek, Petr
dc.relation.ispartof Recent Researches in Automatic Control - 13th WSEAS International Conference on Automatic Control, Modelling and Simulation, ACMOS'11
dc.identifier.isbn 978-1-61804-004-6
dc.date.issued 2011
dc.citation.spage 262
dc.citation.epage 267
dc.event.title 13th WSEAS International Conference on Automatic Control, Modelling and Simulation, ACMOS'11
dc.event.location Lanzarote, Canary Islands
utb.event.state-en Spain
utb.event.state-cs Španělsko
dc.event.sdate 2011-05-27
dc.event.edate 2011-05-29
dc.type conferenceObject
dc.language.iso en
dc.relation.uri http://www.wseas.us/e-library/conferences/2011/Lanzarote/ACMOS/ACMOS-50.pdf
dc.subject Classification en
dc.subject Feature vector en
dc.subject Gunshots en
dc.subject Neural network en
dc.subject Spectral analysis en
dc.description.abstract The paper describes neural network classification of specific audio sources into given categories. Audio sources are represented by various gunshots, from handguns or big bore guns. This article is a follow-up to an existing system for localization of audio sources from security and military areas. The question of successful classification lies in the convenient discrimination of the feature vector in the feature vector space. A set of feature vectors based on power spectral density is evaluated a tested for the best classification of gunshots. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1004784
utb.identifier.obdid 43865892
utb.identifier.scopus 2-s2.0-82555198144
utb.source d-scopus
dc.date.accessioned 2015-06-04T12:55:25Z
dc.date.available 2015-06-04T12:55:25Z
utb.contributor.internalauthor Navrátil, Milan
utb.contributor.internalauthor Křesálek, Vojtěch
utb.contributor.internalauthor Dostálek, Petr
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