Estimation of noise variance from the noisy AR signal and its application in speech enhancement

In a number of applications involving the processing of noisy signals, it is desirable to know a priori the noise variance. We propose here a method of estimating the noise variance from the autoregressive (AR) signal corrupted by the additive white noise. This method first estimates the AR parameters from the high-order Yule-Walker equations and then uses these AR parameters to estimate the noise variance from the low-order Yule-Walker equations. The method is studied for a number of examples of noisy AR signals and its performance is found to be close to the Cramer-Rao lower bound for high signal-to-noise ratios. It is also used in a speech enhancement application where its performance is studied for stationary as well as nonstationary noise conditions. The results are found to be encouraging.