Optimization Scheme of Joint Noise Suppression and Dereverberation Based on Higher-Order Statistics

APSIPA ASC 2012 : Asia-Pacific Signal and Information Processing Association 2012 Annual Summit and Conference, December 3-6, 2012, Hollywood, California, USA.In this paper, we apply the higher-order statistics parameter to automatically improve the performance of blind speech enhancement. Recently, a method to suppress both diffuse background noise and late reverberation part of speech has been proposed combining blind signal extraction and Wiener filtering. However, this method requires a good strategy for choosing the set of its parameters in order to achieve the optimum result and to control the amount of musical noise, which is a common problem in non-linear signal processing. We present an optimization scheme to control the value of Wiener filter coefficients used in this method, which depends on the amount of musical noise generated, measured by higher-order statistics. The noise reduction rate and cepstral distortion are also evaluated to confirm the effectiveness of this scheme

Musical Noise Generation Analysis for Noise Reduction Methods Based on Spectral Subtraction and MMSE STSA Estimation

ICASSP2009: IEEE International Conference on Acoustics, Speech, and Signal Processing, April 19-24, 2009, Taipei, Taiwan.In this paper, we reveal new findings about the generated musical noise in minimum mean-square error short-time spectral amplitude (MMSE STSA) processing. Recently we have proposed a objective metric of musical noise based on kurtosis change ratio on spectral subtraction (SS). Also we found an interesting relationship among the degree of generated musical noise, the shapes of signal-s probability density function, the strength parameter of SS processing. This paper is aimed to automatically evaluate the sound quality of various types of noise reduction methods using kurtosis change ratio. We give a mathematical analysis based on higher-order statistics viewpoint, and lead to a valuable relation in that MMSE STSA has a weakness in speech period distortion rather than noise period, and vice versa in SS