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.
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