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  • In this paper, classification of audio sources is presented to supplement current work on existing system for localization of audio sources. The question of achieving the audio classification lies in the convenient discrimination of the feature vector in the feature vector space. Characteristics based on frequency analysis were chosen and used as feature vector. Artificial neural network was applied in order to classify different audio classes especially from security and military areas, such as different shots and explosions. The information about specific type of a sound can trigger localization process of given audio source. Moreover, it can improve situation when guards get ready for the alert state. This classification method is currently developed as an additional part of the system for audio source hyperbolic localization; the paper also gives some basic structure of that system. Its utilization can be found for additional securing of larger objects like squares or military basis, for instance.
  • In this paper, classification of audio sources is presented to supplement current work on existing system for localization of audio sources. The question of achieving the audio classification lies in the convenient discrimination of the feature vector in the feature vector space. Characteristics based on frequency analysis were chosen and used as feature vector. Artificial neural network was applied in order to classify different audio classes especially from security and military areas, such as different shots and explosions. The information about specific type of a sound can trigger localization process of given audio source. Moreover, it can improve situation when guards get ready for the alert state. This classification method is currently developed as an additional part of the system for audio source hyperbolic localization; the paper also gives some basic structure of that system. Its utilization can be found for additional securing of larger objects like squares or military basis, for instance. (en)
Title
  • Classification of Audio Sources Using Neural Network Applicable in Security or Military Industry
  • Classification of Audio Sources Using Neural Network Applicable in Security or Military Industry (en)
skos:prefLabel
  • Classification of Audio Sources Using Neural Network Applicable in Security or Military Industry
  • Classification of Audio Sources Using Neural Network Applicable in Security or Military Industry (en)
skos:notation
  • RIV/70883521:28140/10:63508954!RIV11-MSM-28140___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • I, Z(MSM7088352102)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
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  • 250901
http://linked.open...ai/riv/idVysledku
  • RIV/70883521:28140/10:63508954
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  • classification; audio; neural network; Fourier transform; spectrum; analysis (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [B9608365FE85]
http://linked.open...v/mistoKonaniAkce
  • San José, USA
http://linked.open...i/riv/mistoVydani
  • Piscataway
http://linked.open...i/riv/nazevZdroje
  • Proceedings 44th Annual 2010 IEEE International Carnahan Conference on Security Technology
http://linked.open...in/vavai/riv/obor
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http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
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  • Navrátil, Milan
  • Dostálek, Petr
  • Křesálek, Vojtěch
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
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  • IEEE Operations Center
https://schema.org/isbn
  • 978-1-4244-7400-4
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  • 28140
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