Noise power estimation based on the probability of speech presence

In this paper, we analyze the minimum mean square error (MMSE) based spectral noise power estimator [1] and present an improvement. We will show that the MMSE based spectral noise power estimate is only updated when the a posteriori signal-to-noise ratio (SNR) is lower than one. This threshold on the a posteriori SNR can be interpreted as a voice activity detector (VAD). We propose in this work to replace the hard decision of the VAD by a soft speech presence probability (SPP). We show that by doing so, the proposed estimator does not require a bias correction and safety-net as is required by the MMSE estimator presented in [1]. At the same time, the proposed estimator maintains the quick noise tracking capability which is characteristic for the MMSE noise tracker, results in less noise power overestimation and is computationally less expensive.