Comparative Study on Various Noise reduction Methods with Decision-Directed a Priori SNR Estimator via Higher-Order Statistics

In this paper, we propose a new theoretical analysis of amount of musical noise generated in several noise reduction methods with a decision-directed a priori SNR estimator using higher-order statistics. In our previous study, a musical noise assessment based on kurtosis has been successfully applied to spectral subtraction and Wiener filter. However, this approach cannot be applied to some high-quality noise reduction methods, namely, the minimum mean-square error short-time spectral amplitude estimator, the minimum mean square error log-spectral amplitude estimator and the maximum a posteriori estimator, because such methods include the decision-directed a priori SNR estimator, which corresponds to a nonlinear recursive (infinite) process for noise power spectral sequences. Therefore, in this paper, we introduce a computationally efficient higher-order-moment calculation method based on generalized Gauss-Laguerre quadrature. We also mathematically clarify the justification of using a typical decision-directed parameter, namely, magic number 0.98, in the three types of the decision-directed-based estimators from a viewpoint of amounts of musical noise and speech distortion. In addition, we perform comparison between these noise reduction methods based on the mathematical analysis and human perception test.
APSIPA ASC 2012 : Asia-Pacific Signal and Information Processing Association 2012 Annual Summit and Conference, December 3-6, 2012, Hollywood, California, USA.