Optimal Cepstrum Smoothing
Författare
Summary, in English
Abstract in Undetermined
The cepstrum of a random process has proven to be a useful tool in a wide range of applications. The common cepstrum estimator based on the periodogram suffers from large variance, and, to a smaller degree, from bias. The variance can be reduced by smoothing. However, the smoothing may be performed in four different domains: the covariance, the spectral, the log-spectral, and the cepstral domain. We present the mean square error (MSE) optimal smoothing kernels in each domain for estimation of the cepstrum. The lower MSE bound of each of the four families of estimators are compared. We also demonstrate how the four MSE optimal estimators differ in robustness.
The cepstrum of a random process has proven to be a useful tool in a wide range of applications. The common cepstrum estimator based on the periodogram suffers from large variance, and, to a smaller degree, from bias. The variance can be reduced by smoothing. However, the smoothing may be performed in four different domains: the covariance, the spectral, the log-spectral, and the cepstral domain. We present the mean square error (MSE) optimal smoothing kernels in each domain for estimation of the cepstrum. The lower MSE bound of each of the four families of estimators are compared. We also demonstrate how the four MSE optimal estimators differ in robustness.
Avdelning/ar
- Matematisk statistik
- Statistical Signal Processing Group
Publiceringsår
2012
Språk
Engelska
Sidor
1290-1301
Publikation/Tidskrift/Serie
Signal Processing
Volym
92
Issue
5
Dokumenttyp
Artikel i tidskrift
Förlag
Elsevier
Ämne
- Probability Theory and Statistics
Nyckelord
- Cepstrum
- Smoothing
Status
Published
Forskningsgrupp
- Statistical Signal Processing
- Stochastics in Medicine
- Statistical Signal Processing Group
ISBN/ISSN/Övrigt
- ISSN: 0165-1684