A Noise Type Classifier for Speech Enhancement in DCT Domain

Traditional speech enhancement algorithms are mostly attenuating filters, which introduce speech distortion when dealing with destructive noise. Some scholars have proposed a double-gain filter for speech enhancement, which considers the two types of noise coefficients, namely constructive noise and destructive noise. Since it is a double-gain filter, the type of noise needs to be classified before using the corresponding filter. Based on the difference of mean square error in double-gain filter, a new noise type classifier is proposed in this paper, which can effectively classify the types of noise before using the double-gain function. The simulation results show that the proposed error double-gain filter has improved speech intelligibility and comprehensive speech quality compared with the existing algorithms.