TY - JOUR
T1 - Signal recognition
T2 - Both components of the short time Fourier transform vs. power spectral density
AU - Gelman, L.
PY - 2003/6
Y1 - 2003/6
N2 - A new feature representation approach was generalised and used for Gaussian recognition. The generalised approach consists of simultaneously using two new recognition features - real and imaginary Fourier components - taking into account the covariance between features. Generalisation of the approach improves recognition effectiveness. An advanced time-frequency technique, the short time Fourier transform, was considered. Covariance and the correlation coefficient between the proposed features were obtained for the first time for arbitrary stationary signals. The recognition effectiveness between the generalised approach and power spectral density was compared. It was shown that power spectral density is not an optimal feature, and represents only a particular case of the generalised approach. The use of power spectral density is optimal if simultaneously the correlation coefficient between Fourier components is equal to zero, and the standard deviations of components are equal. Use of the generalised approach provides an increase in effectiveness in comparison with power spectral density.
AB - A new feature representation approach was generalised and used for Gaussian recognition. The generalised approach consists of simultaneously using two new recognition features - real and imaginary Fourier components - taking into account the covariance between features. Generalisation of the approach improves recognition effectiveness. An advanced time-frequency technique, the short time Fourier transform, was considered. Covariance and the correlation coefficient between the proposed features were obtained for the first time for arbitrary stationary signals. The recognition effectiveness between the generalised approach and power spectral density was compared. It was shown that power spectral density is not an optimal feature, and represents only a particular case of the generalised approach. The use of power spectral density is optimal if simultaneously the correlation coefficient between Fourier components is equal to zero, and the standard deviations of components are equal. Use of the generalised approach provides an increase in effectiveness in comparison with power spectral density.
KW - Covariance and correlation coefficient
KW - Gaussian signal recognition
KW - Likelihood ratio
KW - Power spectral density
KW - Real and imaginary Fourier components
KW - Statistical pattern recognition
UR - http://www.scopus.com/inward/record.url?scp=0042575201&partnerID=8YFLogxK
U2 - 10.1007/s10044-002-0168-4
DO - 10.1007/s10044-002-0168-4
M3 - Article
AN - SCOPUS:0042575201
VL - 6
SP - 91
EP - 96
JO - Pattern Analysis and Applications
JF - Pattern Analysis and Applications
SN - 1433-7541
IS - 2
ER -