Title :
Spectral estimation of quasi-periodic data
Author :
Narayan, Shankar S. ; Burg, John Parker
Author_Institution :
Hitachi Micro Syst. Inc., San Jose, CA, USA
fDate :
3/1/1990 12:00:00 AM
Abstract :
A computationally efficient algorithm for estimating the autocorrelation function and/or the power spectrum of periodic/quasi-periodic time series, when information about its period is available, is described. The autocorrelation matrix obtained from the sample covariance matrix using this method is a good approximation of the maximum-likelihood estimate of the autocorrelation matrix. Simulation results involving sinusoids in noise and speech data are presented
Keywords :
correlation methods; matrix algebra; signal processing; spectral analysis; time series; MLE; autocorrelation matrix; computationally efficient algorithm; maximum-likelihood estimate; noise sinusoids; periodic/quasi-periodic time series; power spectrum; quasi-periodic data; sample covariance matrix; signal processing; speech sinusoids; Amplitude estimation; Autocorrelation; Computational modeling; Entropy; Helium; Parameter estimation; Spectral analysis; Speech analysis; Speech enhancement; Time series analysis;
Journal_Title :
Acoustics, Speech and Signal Processing, IEEE Transactions on
