J4 ›› 2011, Vol. 46 ›› Issue (7): 83-86.
• Articles •
ZHANG Ning-xian, GUO Min*, MA Miao
The AR model for three different strains of fruit fly wings vibration sound is established respectively. The AR coefficients and the variance of white noise sequence are extracted as the feature, then the sound of the three different strains of fruit fly wings vibration sound is classified by support vector machine(SVM). The order of AR model is determined by using AIC criterion, and the parameters of AR model is estimated by Burg method, and then the heavy tailed RBF is used as the kernel function in SVM to implement the feature extraction and classification of the different strains of fruit fly wings vibration sound. The experiomental results show that the classification accuracy rate of the three strains of fruit fly wings vibration sound is more than 88％.
AR model; support vector machine; fruit fly wings vibration sound
ZHANG Ning-xian, GUO Min*, MA Miao. Classification of fruit fly wings vibration sound based on the AR model and SVM[J].J4, 2011, 46(7): 83-86.
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