A low-complexity noise estimation algorithm based on smoothing of noise power estimation and estimation bias correction

This paper presents a low-complexity algorithm for tracking the noise spectral variance of speech contaminated by non-stationary noise sources. The proposed algorithm is based upon a recursive refinement process in which each step of the algorithm expectation of the instantaneous noise power is calculated based on information from the incoming signal and the current estimated distribution parameters, and estimation of the distribution parameter is refined accordingly to incorporate the expectation results. A bias estimation correction method is also introduced in the algorithm to avoid estimation errors that may occur when there is a significant mismatch between the statistics of the input signal and the current estimated distribution parameters. The proposed algorithm is compared to the Minimum Statistics method and it is found that the proposed algorithm achieves similar or better performances for various noise conditions and SNR settings.