DocumentCode :
2937796
Title :
Habituation based neural classifiers for spatio-temporal signals
Author :
Stiles, Bryan W. ; Ghosh, Joydeep
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX, USA
Volume :
5
fYear :
1995
fDate :
9-12 May 1995
Firstpage :
3407
Abstract :
Based on the habituation mechanism found in biological neural systems, novel dynamic neural networks are proposed for recognizing temporal patterns. The specific task considered in this paper is the classification of whale songs from passive sonar data, but the networks are also readily applicable to other temporal pattern recognition problems. The fact that the networks designed operate dynamically is important, because it makes the goal of real time data analysis possible
Keywords :
aquaculture; biocommunications; encoding; neural nets; pattern classification; sonar signal processing; underwater sound; biological neural systems; dynamic neural networks; encoding; habituation based neural classifiers; habituation mechanism; passive sonar data; real time data analysis; spatio-temporal signals; temporal pattern recognition; whale songs classification; Biological information theory; Data analysis; Data preprocessing; Delay effects; Encoding; Equations; Neural networks; Pattern recognition; Sonar applications; Whales;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on
Conference_Location :
Detroit, MI
ISSN :
1520-6149
Print_ISBN :
0-7803-2431-5
Type :
conf
DOI :
10.1109/ICASSP.1995.479717
Filename :
479717
Link To Document :
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