Abstract: In the context of the rapid development of network information, the demand of different music lovers for music information retrieval is also increasing, and music classification has become an important research subject. This paper proposes a fast classification method of DUPSO-DSVM folk songs, which combines dissipative uniform particle swarm optimization (DUPSO) with distance sorted SVM (DSVM). This method uses DUPSO algorithm to optimize the penalty coefficient C and kernel function parameter g of SVM, and uses DSVM algorithm to optimize the parameter optimization time of DUPSO algorithm. The experimental results show that, DUPSO-SVM algorithm has a classification accuracy of 84%. After using DUPSO-DSVM algorithm, the training time of the algorithm only accounts for 26.26% of the unused DUPSO-DSVM algorithm, but it still maintains a high classification accuracy. 

Key words: folk song classification, feature extraction, DUPSO algorithm, support vector machine, support vector preselecting