Filtering of colored noise for speech enhancement and coding

Author

Gibson, Jerry D. ; Koo, Boneung ; Gray, Steven D.

Author_Institution

Dept. of Electr. Eng., Texas A&M Univ., College Station, TX, USA

Volume

39

Issue

8

fYear

1991

fDate

8/1/1991 12:00:00 AM

Firstpage

1732

Lastpage

1742

Abstract

Scalar and vector Kalman filters are implemented for filtering speech contaminated by additive white noise or colored noise, and an iterative signal and parameter estimator which can be used for both noise types is presented. Particular emphasis is placed on the removal of colored noise, such as helicopter noise, by using state-of-the-art colored-noise-assumption Kalman filters. The results indicate that the colored noise Kalman filters provide a significant gain in signal-to-noise ratio (SNR), a visible improvement in the sound spectrogram, and an audible improvement in output speech quality, none of which are available with white-noise-assumption Kalman and Wiener filters. When the filter is used as a prefilter for linear predictive coding, the coded output speech quality and intelligibility are enhanced in comparison to direct coding of the noisy speech

Keywords

Kalman filters; encoding; filtering and prediction theory; random noise; speech analysis and processing; speech intelligibility; Kalman filters; additive white noise; colored noise; iterative signal estimator; linear predictive coding; parameter estimator; scalar algorithms; signal-to-noise ratio; speech coding; speech enhancement; speech intelligibility; speech quality; vector algorithms; Acoustic noise; Additive white noise; Colored noise; Filtering; Helicopters; Parameter estimation; Signal to noise ratio; Speech coding; Speech enhancement; Wiener filter;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.91144

Filename

91144