Autoregressive models for noisy speech signals | IEEE Conference Publication | IEEE Xplore

Autoregressive models for noisy speech signals


Abstract:

Linear prediction is a well extended technique for transmission, synthesis and recognition. However when the signal is corrupted by noise, the estimation of the auto-regr...Show More

Abstract:

Linear prediction is a well extended technique for transmission, synthesis and recognition. However when the signal is corrupted by noise, the estimation of the auto-regressive model is known to be biaised. This paper is devoted to methods allowing a reduction of this bias. We will consider first a global method, in which the Yule Walker equations are modified to take into account the variance of an additive white noise. The problem becomes non-linear and is solved recursively. In a second approach, we will examine a time - recursive method based on Kalman filtering.
Date of Conference: 30 March 1981 - 01 April 1981
Date Added to IEEE Xplore: 29 January 2003
Conference Location: Atlanta, GA,USA

References

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