Linear prediction based on Teager-Kaiser energy function and application to speech modeling and spectral analysis

Author

Chitrapu, Prabhakar Rao

Author_Institution

Dialogic Corp., Parsippany, NJ, USA

Volume

2

fYear

1996

fDate

12-15 May 1996

Firstpage

49

Abstract

In this paper, the classic problem of determining the optimal linear FIR filter that minimizes an appropriate norm of the filter output is addressed, with the signal norm being the recently introduced Teager-Kaiser Energy norm. Normal equations are derived and conditions for the existence of a solution are given. This theory is then applied to LPC modeling of speech signals and compared with the standard LPC results, where the L²-norm of the filter output is minimized. It is observed that the TK-LPC spectra have enhanced formant structure and expanded bandwidth, which could be useful in increasing the perceptual quality of TK-LPC coders. Finally, TK-LPC analysis was applied to spectral estimation, where it is observed that the TK-LPC spectra had higher resolution

Keywords

FIR filters; Hermitian matrices; Toeplitz matrices; prediction theory; spectral analysis; speech processing; LPC modeling; Teager-Kaiser energy function; linear prediction; normal equations; optimal linear FIR filter; spectral analysis; spectral estimation; speech modeling; Autocorrelation; Equations; Finite impulse response filter; Linear predictive coding; Nonlinear filters; Predictive models; Signal analysis; Spectral analysis; Speech analysis; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1996. ISCAS '96., Connecting the World., 1996 IEEE International Symposium on

Conference_Location

Atlanta, GA

Print_ISBN

0-7803-3073-0

Type

conf

DOI

10.1109/ISCAS.1996.540349

Filename

540349