Chongqing 400044，China)
Abstract: To improve the precision of extracting feature and efficiency of classification and recognition from heart sound, the method of Discrete Wavelet Transform Mel Frequency Cepstrum Coefficients (DWPTMFCC) combined with improved Gaussian Mixture Model (GMM) is used for classification and recognition of heart sound. Firstly, the new feature parameter is formed by using wavelet packet transform instead of Fourier transform and Mel filter group on the basis of the extraction method of MFCC; Secondly, to overcome the shortcoming of K-means algorithm which is used in the parameters initialization process of traditional GMM, Weighted Optional Fuzzy C-Means (WOFCM) algorithm is proposed; Finally, the feature parameters are input into improved GMM for recognition. The clinical data experimental diagnosis and test results show that the method not only can effectively extract heart sound feature, but also have better recognition performance compared with traditional GMM.