The Regional Style Classification of Chinese Folk Songs Based on GMM-CRF Model
Juan Li, Jianhang Ding, Xinyu Yang · 2017
Music regional style classification aims to divide the traditional folk songs according to different geographies. Research on the regional style classification of music contributes to digging into the creation rules of traditional folk songs and is of great significance to geographical type retrieval. This paper combines the temporal model to encode the musical structures based on the analysis of the characteristics of Chinese traditional folk songs. Conditional Random Field (CRF) is utilized to establish the model of folk songs for the first time. The state function and the transfer function of the CRF are calculated by the classical clustering algorithm Gaussian Mixture Model (GMM) to estimate the label sequence. In contrast to previous music classification methods (such as SVM, KNN, etc.), the accuracy of the regional style classification of folk songs increases in a range of 4.6% ~ 18.13%, and the best performance is achieved by the GMM-CRF model in our experiment.