Research on Music Classification Based on MFCC and BP Neural Network

被引:0
作者
LiuYongchun [1 ]
Hong, Song [1 ]
Jing, Yang [2 ]
机构
[1] Sichuan Univ Sci & Engn, Sch Automat & Elect Informat, Zigong, Sichuan Provinc, Peoples R China
[2] Sichuan Univ Sci Engn Zigong, Sch Foreign Language, Zigong, Sichuan, Peoples R China
来源
PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON INFORMATION, ELECTRONICS AND COMPUTER | 2014年 / 59卷
关键词
BP neural network; MFCC feature extraction; music classification; hidden Markov model; SPEECH;
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Because of the diversity and uncertainty of music, the classification rate and accuracy are both lower for the traditional classification methods in the large-scale music classification application. A based on BP neural network (BPNN) music classification method proposed in this paper can improve this performance, which extracts the feature parameters of music through mel frequency cepstrum coefficient(MFCC) firstly, and then the BPNN is used to train feature signals and establish the optimal classifier model, finally classifies the test music dataset. The average classification accuracy rate is up to 90.2%, and higher 7% than the HMM classification method by simulation experiments for the folk, classical, rock and pop different types of music, therefore, the results show that the BPNN is a quite effective music type classification method.
引用
收藏
页码:129 / 132
页数:4
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