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Application of Speech Signals to Deterministic Signal Modeling Techniques

by D. Vijaya Lakshmi, G. Gayatri
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 100 - Number 16
Year of Publication: 2014
Authors: D. Vijaya Lakshmi, G. Gayatri
10.5120/17610-8296

D. Vijaya Lakshmi, G. Gayatri . Application of Speech Signals to Deterministic Signal Modeling Techniques. International Journal of Computer Applications. 100, 16 ( August 2014), 30-37. DOI=10.5120/17610-8296

@article{ 10.5120/17610-8296,
author = { D. Vijaya Lakshmi, G. Gayatri },
title = { Application of Speech Signals to Deterministic Signal Modeling Techniques },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 100 },
number = { 16 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 30-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume100/number16/17610-8296/ },
doi = { 10.5120/17610-8296 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:30:08.786333+05:30
%A D. Vijaya Lakshmi
%A G. Gayatri
%T Application of Speech Signals to Deterministic Signal Modeling Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 100
%N 16
%P 30-37
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Signal modeling is concerned with the representation of signals. The modeled signal consists of parameters, using which the original signal can be reconstructed or recovered. When once it is possible to accurately model a signal, then it becomes possible to perform important signal processing tasks such as signal compression, interpolation, prediction. The models used are AR (Auto Regressive) or All-Pole model, MA (Moving Average) or All-Zero model, ARMA (Auto Regressive Moving Average) or Pole-Zero model. Various methods have been suggested for the coefficients determination among which are Prony, Pade, Shank, Autocorrelation, Covariance techniques. In this paper, these techniques are applied for speech signals and comparisons are carried out. The comparisons are entirely based on the value of the coefficients obtained.

References
  1. Monson H. Hayes, "Statistical Digital Signal Processing and Modeling" , John Wiley & Sons, INC.
  2. Thomas F. Quatieri "Discrete Time Speech Signal Processing", Prentice-Hall, INC.
  3. Sanjit. K. Mitra, "Digital Signal Processing", Mc Graw-Hill Companies, INC,2006 New York.
  4. Dimitris G. Manolakis, "Digital Signal Processing principles, Algorithms and Applications", PHI.
  5. Peyton Z. Peebles, "Probability, Random Variables and Random Signal Principles", McGraw-Hill, INC.
  6. John G. Proakis, Dimitris G. Manolakis, "Digital Signal Processing Principles, Algorithms and Applications", PHI.
  7. B. S. Atal and J. R. Remda, "A new model of LPC excitation for producing natural-sounding speech at loe bit rates,"Proc. IEEE Int. Conf. on Acoust. , Speech, Sig. Proc. , Paris,pp. 614-617, May 1982.
Index Terms

Computer Science
Information Sciences

Keywords

Pade Prony Shank Auto Regressive Moving Average Autoregressive Moving Average.

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