DocumentCode
1061917
Title
Spectral estimation of quasi-periodic data
Author
Narayan, Shankar S. ; Burg, John Parker
Author_Institution
Hitachi Micro Syst. Inc., San Jose, CA, USA
Volume
38
Issue
3
fYear
1990
fDate
3/1/1990 12:00:00 AM
Firstpage
512
Lastpage
518
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;
fLanguage
English
Journal_Title
Acoustics, Speech and Signal Processing, IEEE Transactions on
Publisher
ieee
ISSN
0096-3518
Type
jour
DOI
10.1109/29.106869
Filename
106869
Link To Document