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Linear prediction based speech (LPC) analysis is known to be sensitive to the presence of additive noise. In this paper, we present a noise-compensated method for LPC analysis which ensures good spectral matching between the original speech spectrum and the autoregressive (AR) model spectrum. In this method, the noise periodogram is obtained first by applying a simplified noise power spectral density...
This paper presents a scheme for the identification of minimum-phase autoregressive moving average (ARMA) systems in the presence of noise. For the identification of the AR part, we propose to repeat the correlation operation on an enhanced autocorrelation function of the observed signal based on a decision criterion and then employ the resultant to an extended form of Yule-Walker equations. A frequency...
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