New Approach to Classification of Chinese Folk Music Based on Eztension of HMM
Recently, class labels are commonly used to structure the increasing amounts of music available in digital form on the Web and are important for music information retrieval. An evaluation for automatic classification of Chinese folk music according to an audio taxonomy is presented. The audio taxonomy is organized as hierarchical, resulting in good coverage of Chinese folk music.Continuous Hidden Markov Model(CHMM) have been widely used to model the temporal evolution of dynamic sounds, especially music signal, whereas with an obvious drawback that the probability of time spends in a particular state, or state occupancy is geometrically distributed, which is not the case in real music signal. In this paper, we presented two extensions of standard HMM: Hidden semi-Markov Model(HSMM), and Segmentation Duration-Based HMM(SDBHMM), providing a comparison among them and Continuous Hidden Markov Model(CHMM). The former extension has been presented in speech recognition and we proposed the later one originally. Our result show that SDBHMM could achieve classification accuracy of 92.49% approximately and HSMM with 90.02%, both of which outperform standard CHMM.
XiaoBing Liu DeShun Yang XiaoOu Chen
Institute of Computer Science and Technology, Peking University, Beijing 100871, China
国际会议
2008 International Conference on Audio，Language and Image Processing(2008国际声音、语言、图像过程大会)
镇江
英文
1172-1179
2008-07-07（万方平台首次上网日期，不代表论文的发表时间）