A comparative performance evaluation of adaptive ARMA spectral estimation methods for noisy speech

Author

Basu, Anjan ; Paliwal, K.K.

Author_Institution

Tata Inst. of Fundamental Res., Bombay, India

fYear

1988

fDate

11-14 Apr 1988

Firstpage

691

Abstract

The problem of adaptive estimation of linear prediction (LP) coefficients from noisy speech is considered. Performance of three adaptive ARMA spectral estimation algorithms are studied for this purpose: the recursive extended least squares (RELS) algorithm, the recursive maximum likelihood (RML) algorithm, and the overdetermined recursive instrumental variable (ORIV) algorithm. To put them in proper perspective, the normalized LMS (NLMS) has also been considered. The ORIV algorithm is found to be the best in terms of Itakura distance from the ideal LP coefficients and the power spectral density estimation. The RML algorithm is found to be robust in highly noisy cases

Keywords

estimation theory; noise; spectral analysis; speech analysis and processing; Itakura distance; adaptive ARMA spectral estimation; adaptive estimation; linear prediction coefficients; noisy speech; overdetermined recursive instrumental variably algorithm; performance evaluation; power spectral density estimation; recursive extended least squares algorithm; recursive maximum likelihood algorithm; Acoustic noise; Least squares approximation; Parameter estimation; Peak to average power ratio; Signal processing; Speech analysis; Speech coding; Speech enhancement; Speech processing; White noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 International Conference on

Conference_Location

New York, NY

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.1988.196680

Filename

196680