
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/">
  <dc:contributor>Nikolić, Vlastimir 1954-</dc:contributor>
  <dc:contributor>Antić, Dragan 1963-</dc:contributor>
  <dc:contributor>Ćojbašić, Žarko 1968-</dc:contributor>
  <dc:contributor>Simonović, Miloš 1973-</dc:contributor>
  <dc:contributor>Ćirić, Ivan 1980-</dc:contributor>
  <dc:date>2020</dc:date>
  <dc:language>srp</dc:language>
  <dc:format>[17], 165 listova</dc:format>
  <dc:format>14450068 bytes</dc:format>
  <dc:title xml:lang="srp">Optimalno prepoznavanje i lokalizacija izvora zvuka primenom metoda veštačke inteligencije</dc:title>
  <dc:identifier>https://phaidrani.ni.ac.rs/o:1656</dc:identifier>
  <dc:identifier>cobiss:13554185</dc:identifier>
  <dc:identifier>thesis:7510</dc:identifier>
  <dc:creator>Kovandžić, Marko 1974-</dc:creator>
  <dc:rights>http://creativecommons.org/licenses/by/2.0/at/legalcode</dc:rights>
  <dc:description xml:lang="eng">Тhe subject of the thesis is sound source recognition and sound source localization, in real
conditions, using artificial intelligence algorithms. The main goal is optimal procedure for sound
source observation using artificial neural networks for signal procesing, because of their extreme
procesing speed. It has to provide implementation of hybrid system capable to recognize and
locate sound source in the presence of disturbances. For the training and the testing of neural
networks two sets of data are provided, from two different experiments, and to increase
robustness genetic algorithm is applied. The results of the investigation will contribute the
existing body of acoustic observation knowledge.</dc:description>
  <dc:description xml:lang="srp">Biografija autora: list 165;Bibliografija: listovi 154-164.  Datum odbrane: 14.02.2020. Automatic control and robotics</dc:description>
  <dc:type>info:eu-repo/semantics/bachelorThesis</dc:type>
</oai_dc:dc>