Quality Analysis and Optimization of the MAP-based Noise Power Spectral Density Tracker

It has been lately shown that noise tracking and speech denoising can be improved by a postprocessor established on a maximum a-posteriori based (MAP-B) noise power spectral density (PSD) estimation algorithm. In the current contribution we investigate the MAP-B estimator by carrying out a quality analysis comprising the following three steps. First, we analyse the estimator with respect to unbiasedness and consistency, second, the tracking ability in non-stationary noise is investigated, and finally, the sensitivity of the MAP-B noise tracker with respect to estimation errors in the preprocessing stage is considered. The findings are used to develop an optimized MAP-B postprocessor. The performance comparison with the original MAP-B tracker indeed reveals improved performance at high signal-to-noise ratios.