Acoustic spectral estimation using higher order statistics

Author

Dubnov, Shlomo ; Tishby, Naftali

Author_Institution

Inst. for Comput. Sci., Hebrew Univ., Jerusalem, Israel

fYear

1994

fDate

9-13 Oct 1994

Firstpage

1

Abstract

Assuming an autoregressive (AR) filter model driven by a non-Gaussian white noise, we formulate a general parameter estimation problem. A maximum likelihood solution gives an AR estimate of the filter and the probability distribution function parameters for non-Gaussian input. The proposed method is optimal in the information theoretic sense, giving the most probable model for the source and filter under the higher order statistics constrains of the observed signal. Analysis of human singing voices and musical instruments is presented and its acoustic interpretation is discussed

Keywords

acoustic signal processing; acoustic spectral estimation; autoregressive filter model; higher order statistics; human singing voices; maximum likelihood; musical instruments; nonGaussian white noise; parameter estimation; probability distribution; Constraint theory; Higher order statistics; Humans; Information filtering; Information filters; Maximum likelihood estimation; Parameter estimation; Probability distribution; Speech analysis; White noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1994. Vol. 3 - Conference C: Signal Processing, Proceedings of the 12th IAPR International Conference on

Conference_Location

Jerusalem

Print_ISBN

0-8186-6275-1

Type

conf

DOI

10.1109/ICPR.1994.577110

Filename

577110