DocumentCode :
2692073
Title :
Bispectral Gammatone Cepstral Coefficient based Neural Network Classifier
Author :
Mohankumar, K. ; Supriya, M.H. ; Saseendran Pillai, P.R.
Author_Institution :
Dept. of Electron., Cochin Univ. of Sci. & Technol., Kochi, India
fYear :
2015
fDate :
23-25 Feb. 2015
Firstpage :
1
Lastpage :
5
Abstract :
The estimation of the power spectrum of discrete-time signals is one of the most fundamental and useful tools in signal processing. However, there are practical situations where one needs to look beyond the power spectrum, especially to extract information regarding the phase relations and deviations from Gaussianity. This has created considerable interest in the use of higher order spectra such as bispectrum, for the analysis of signals, particularly in the presence of additive Gaussian noise. This paper examines the use of Gammatone Cepstral Coefficients computed from the spectrum reconstructed from the bispectrum of the signal as a feature set for underwater target classification. A prototype Neural Network classifier with back propagation algorithm has been trained with the proposed feature set and the performance has been evaluated, which has yielded acceptable classification results.
Keywords :
AWGN; backpropagation; discrete time systems; neural nets; pattern classification; signal processing; Gaussianity; additive Gaussian noise; back propagation algorithm; bispectral gammatone cepstral coefficient; discrete-time signals; feature set; gammatone cepstral coefficients; neural network classifier; power spectrum estimation; signal bispectrum; signal processing; underwater target classification; Artificial neural networks; Cepstral analysis; Classification algorithms; Filter banks; Mutual information; Noise; Training; Artificial Neural Networks; BGTCC; Bispectrum; Higher Order Spectral Analysis; Target classifier;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Underwater Technology (UT), 2015 IEEE
Conference_Location :
Chennai
Print_ISBN :
978-1-4799-8299-8
Type :
conf
DOI :
10.1109/UT.2015.7108321
Filename :
7108321
Link To Document :
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