Title Approximate Optimal Periodogram Smoothing for Cepstrum Estimation using a Penalty Term
Authors Johan Sandberg, Maria Sandsten
Full-text Available as PDF, Restricted Access
Alternative Location http://www.eurasip.org/Proc...
Publication Proceedings of the EUSIPCO, European Signal Processing Conference 2010
Year 2010
Pages 363 - 367
Document type Conference paper
Conference name 18th European Signal Processing Conference (EUSIPCO-2010)
Conference Date August 23-27 2010
Conference Location Aalborg, Denmark
Status Published
Quality controlled Yes
Language eng
Publisher EURASIP
Abstract English The cepstrum of a random process is useful in many applications. The cepstrum is usually estimated from the periodogram. To reduce the mean square error (MSE) of the estimator, the periodogram may be smoothed with a kernel function. We present an explicit expression for a kernel function which is approximatively MSE optimal for cepstrum estimation. A penalty term has to be added to the minimization problem, but we demonstrate how the weighting of the penalty term can be chosen. The performance of the estimator is evaluated on simulated processes. Since the MSE optimal smoothing kernel depends on the true covariance function, we give an example of a simple data driven method.
ISBN/ISSN/Other ISSN: 2076-1465

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