BIAS correction methods for adaptive recursive smoothing with applications in noise PSD estimation

Abstract

Due to the low computational complexity and the low memory consumption, first-order recursive smoothing is a technique often applied to estimate the mean of a random process. For instance, recursive smoothing is used in noise power estimators where adaptively changing smoothing factors are used instead of fixed ones to prevent the speech power from leaking into the noise estimate. However, in general, the usage of adaptive smoothing factors leads to a biased estimate of the mean. In this paper, we propose a novel method to correct the bias evoked by adaptive smoothing factors. We compare this method to a recently proposed compensation method in terms of the log-error distortion using real world signals for two noise power estimators. We show that both corrections reduce the distortion measure in noisy speech while the novel method has the advantage that no iteration is required for determining the correction factor.