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Approximate Optimal Periodogram Smoothing for Cepstrum Estimation using a Penalty Term

Sandberg, Johan LU and Sandsten, Maria LU (2010) 18th European Signal Processing Conference (EUSIPCO-2010) In Proceedings of the EUSIPCO, European Signal Processing Conference 2010 p.363-367
Abstract
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.
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Proceedings of the EUSIPCO, European Signal Processing Conference 2010
pages
363 - 367
publisher
EURASIP
conference name
18th European Signal Processing Conference (EUSIPCO-2010)
external identifiers
  • scopus:84863795920
ISSN
2076-1465
language
English
LU publication?
yes
id
9730c889-a413-489e-becf-5b6baa72f843 (old id 1718666)
alternative location
http://www.eurasip.org/Proceedings/Eusipco/Eusipco2010/Contents/papers/1569291911.pdf
date added to LUP
2010-11-19 07:59:39
date last changed
2018-05-29 10:35:40
@inproceedings{9730c889-a413-489e-becf-5b6baa72f843,
  abstract     = {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.},
  author       = {Sandberg, Johan and Sandsten, Maria},
  booktitle    = {Proceedings of the EUSIPCO, European Signal Processing Conference 2010},
  issn         = {2076-1465},
  language     = {eng},
  pages        = {363--367},
  publisher    = {EURASIP},
  title        = {Approximate Optimal Periodogram Smoothing for Cepstrum Estimation using a Penalty Term},
  year         = {2010},
}