Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(107.6KB)
This is an Open Access publication published under CSC-OpenAccess Policy.

PUBLICATIONS BY COUNTRIES

Top researchers from over 74 countries worldwide have trusted us because of quality publications.

United States of America
United Kingdom
Canada
Australia
Malaysia
China
Japan
Saudi Arabia
Egypt
India
Consistent Nonparametric Spectrum Estimation Via Cepstrum Thresholding
Moram Venkatanarayana , T. Jayachandra Prasad
Pages - 292 - 303     |    Revised - 30-11-2010     |    Published - 20-12-2010
Volume - 4   Issue - 5    |    Publication Date - December 2010  Table of Contents
MORE INFORMATION
KEYWORDS
Cepstrum, Cramer Rao Lower Bound, Consistency, Unbiasedness
ABSTRACT
For stationary signals, there are number of power spectral density estimation techniques. The main problem of power spectral density (PSD)estimation methods is high variance. Consistent estimates may be obtained by suitable processing of the empirical spectrum estimates (periodogram). This may be done using window functions. These methods all require the choice of a certain resolution parameters called bandwidth. Various techniques produce estimates that have a good overall bias Vs variance tradeoff. In contrast, smooth components of this spectral required a wide bandwidth in order to achieve a significant noise reduction. In this paper, we explore the concept of cepstrum for non parametric spectral estimation. The method developed here is based on cepstrum thresholding for smoothed non parametric spectral estimation. The algorithm for Consistent Minimum Variance Unbiased Spectral estimator is developed and implemented, which produces good results for Broadband and Narrowband signals.
CITED BY (2)  
1 Sridhar, B., & Tadisetty, S. (2015, June). ERLS non parametric spectrum sensing for CR. In Advance Computing Conference (IACC), 2015 IEEE International (pp. 185-190). IEEE.
2 Venkatanarayana, M. (2014). Variance reduction in power spectrum using cepstral thresholding appraoch.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 iSEEK 
5 Socol@r  
6 Scribd 
7 SlideShare 
8 PDFCAST 
9 PdfSR 
1 Y.Ephraim and M.Rahim, “On second-order statistics and linear estimation of cepstral Coefficiets”, IEEE Trans.Speech Audio Processing, vol.7, no.2, pp.162-176, 1999.
2 A.H.Gray Jr., “Log spectra of Gaussian signals”, Journal Acoustical Society of America, Vol.55, No.5, May 1974.
3 Grace Wahba, “Automatic Smoothing of the Log Periodogram”, JASA, Vol.75, Issue 369, Pages: 122-132.
4 Herbert T. Davis and Richard H. Jones, “Estimation of the Innovation Variance of a Stationary Time Series”: Journal of the American Statistical Association, Vol. 63, No. 321 (Mar., 1968), pp. 141- 149.
5 Masanobu Taniguchi, “On selection of the order of the spectral density model for a stationary Process”, Ann.Inst.Statist.Math.32 (1980), part-A, 401-419.
6 Yariv Ephraim and David Malah, “Speech Enhancement Using a Minimum Mean Square Error Short-Time Spectral Amplitude Estimator”, IEEE trans., on ASSP, vol.32, No.6, December, 1984.
7 P.Stoica and N. Sandgren, “Smoothed nonparametric spectral estimation via cepstrum Thresholding” IEEE Signal Processing Magazine, November, 2006, pp. 34-45.
8 D. G. Childers D. P. SLciAner and R. C. Kernemit, “The Cepstrum: A Guide to Processing”, Proceedings of the IEEE, Vol. 65, no. 10, October 1977.
9 P. Moulin, “Wavelet thresholding techniques for power spectrum estimation” IEEE Trans. Signal Processing, vol. 42, no. 11, pp. 3126–3136, 1994.
10 H.-Y. Gao, “Choice of thresholds for wavelet shrinkage estimate of the spectrum” J. Time Series Anal., vol. 18, no. 3, pp. 231–251, 1997.
11 A.T. Walden, D.B. Percival, and E.J. McCoy, “Spectrum estimation by wavelet thresholding of multitaper estimators,” IEEE Trans. Signal Processing, vol. 46, no. 12, pp. 3153–3165, 1998.
12 A.R. Ferreira da Silva, “Wavelet denoising with evolutionary algorithms” in Proc. Digital Sign., 2005, vol. 15, pp. 382–399.
13 B.P. Bogert, M.J.R. Healy, and J.W. Tukey, “The quefrency analysis of time series for echoes: Cepstrum, pseudo-autocovariance, cross-cepstrum and saphe cracking,” in Time Series Analysis, M. Rosenblatt, Ed. Ch. 15, 1963, pp. 209–243.
14 E.J. Hannan and D.F. Nicholls, “The estimation of the prediction error variance,”J. Amer. Statistic. Assoc., vol. 72, no. 360, pp. 834–840, 1977.
15 M. Taniguchi, “On estimation of parameters of Gaussian stationary processes,” J. Appl. Prob., vol. 16, pp. 575–591, 1979.
16 PETR SYSEL JIRI MISUREC, “Estimation of Power Spectral Density using Wavelet Thresholding”, Proceedings of the 7th WSEAS International Conference on circuits, systems, electronics, control and signal processing (CSECS'08).
17 Petre Stoica and Niclas Sandgren, “Cepstrum Thresholding Scheme for Nonparametric Estimation of Smooth Spectra”, IMTC 2006 - Instrumentation and Measurement Technology Conference Sorrento, Italy 24-27 April 2006.
18 P. Stoica and R.Moses, “Spectral Analysis of Signals”, Englewood Cliffs, NJ: Prentice Hall, 2005.
19 John G. Proakis and Dimitris G. Manolakis, “Digital Signal Processing: Principles and Applications”, PHI publications, 2nd edition, Oct 1987.
20 M.B.Priestley, “Spectral Analysis and Time series” Volume-1, Academic Press, 1981.
21 Alexander D.Poularikas and Zayed M.Ramadan, “Adaptive Filtering Primer with Matlab”, CRC Press, 2006
Associate Professor Moram Venkatanarayana
K.S.R.M.College of Engg., - India
narayanamoram@yahoo.co.in
Dr. T. Jayachandra Prasad
RGMCET - India