On Second Order Statistics and Linear Estimation of Cepstral Coefficients – Yariv Ephraim (Department of Electrical and Computer Engineering, George Mason University)

When:
October 29, 1997 all-day
1997-10-29T00:00:00-05:00
1997-10-30T00:00:00-05:00

Abstract
Explicit expressions for the second order statistics of cepstral components representing clean and noisy signal waveforms are derived. The noise is assumed additive to the signal, and the spectral components of each process are assumed statistically independent complex Gaussian random variables. The key result developed here is an explicit expression for the cross-covariance between the log-spectra of the clean and noisy signals. In the absence of noise, this expression is used to show that the covariance matrix of cepstral components representing a vector of N signal samples, approaches a fixed, signal independent, diagonal matrix at a rate of 1/(N*N). In addition, the cross-covariance expression is used to develop an explicit linear minimum mean square error estimator for the clean cepstral components given noisy cepstral components. Recognition results on the ten English digits using the fixed covariance and linear estimator are presented.* Joint work with Dr. Mazin Rahim of AT&T Labs.
Biography
Yariv Ephraim received the D.Sc. in Electrical Enginering in 1984 from the Technion-Israel Institute of Technology. He was a Research Scholar at Stanford University from 1984 through 1985, and a Member of Technical Staff at AT&T Bell Laboratories from 1985 until 1993. He has been with George Mason University since 1991 where he currently is an Associate Professor of Electrical and Computer Engineering. His current research interests are statistical signal processing with applications to speech signals and array processing. He was elected Fellow of the Institute of Electrical and Electronic Engineers in 1994.

Johns Hopkins University

Johns Hopkins University, Whiting School of Engineering

Center for Language and Speech Processing
Hackerman 226
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Center for Language and Speech Processing