TY - JOUR
T1 - Based on Gaussian Mixture Model to Explore the General Characteristic and Make Recognition of XinTianYou
AU - Li, Juan
AU - Wu, Kai
AU - Wang, Yinrui
AU - Yang, Xinyu
N1 - Publisher Copyright:
© 2016 Owned by the authors, published by EDP Sciences.
PY - 2016/4/26
Y1 - 2016/4/26
N2 - XinTianYou, a folk song style from Shannxi province in China, is considered to be a precious traditional culture heritage. Research about XinTianYou is important to the overall Chinese folk music theory and is potentially quite useful for the culture preservation and applications. In this paper, we analyze the general characteristics of XinTianYou by using the pitch, rhythm features and the combination of these two features. First, we use the Gaussian Mixture Model (GMM) to cluster the XinTianYou audio based on pitch and rhythm respectively, and analyze the general characteristics of XinTianYou based on the clustering result. Second, we propose an improved Features Relative Contribution Algorithm (CFRCA) to com-pare the contributions of pitch and rhythm. Third, the probability of a song being XinTianYou can be estimated based on the GMM and the cosine similarity distance. The experimental results show that XinTianYou has large pitch span and large proportion of high pitch value (about 22%). Regarding the rhythm, we find that moderato is dominated while lento-moderato keep a similar ratio as moderato-allegro. The similarity between pitch features of all XinTianYou songs is more significant than rhythm features. Additionally, the average accuracy of XinTianYou recognition reaches 92.4% based on our method.
AB - XinTianYou, a folk song style from Shannxi province in China, is considered to be a precious traditional culture heritage. Research about XinTianYou is important to the overall Chinese folk music theory and is potentially quite useful for the culture preservation and applications. In this paper, we analyze the general characteristics of XinTianYou by using the pitch, rhythm features and the combination of these two features. First, we use the Gaussian Mixture Model (GMM) to cluster the XinTianYou audio based on pitch and rhythm respectively, and analyze the general characteristics of XinTianYou based on the clustering result. Second, we propose an improved Features Relative Contribution Algorithm (CFRCA) to com-pare the contributions of pitch and rhythm. Third, the probability of a song being XinTianYou can be estimated based on the GMM and the cosine similarity distance. The experimental results show that XinTianYou has large pitch span and large proportion of high pitch value (about 22%). Regarding the rhythm, we find that moderato is dominated while lento-moderato keep a similar ratio as moderato-allegro. The similarity between pitch features of all XinTianYou songs is more significant than rhythm features. Additionally, the average accuracy of XinTianYou recognition reaches 92.4% based on our method.
UR - https://www.scopus.com/pages/publications/84969277549
U2 - 10.1051/matecconf/20165603001
DO - 10.1051/matecconf/20165603001
M3 - 会议文章
AN - SCOPUS:84969277549
SN - 2261-236X
VL - 56
JO - MATEC Web of Conferences
JF - MATEC Web of Conferences
M1 - 03001
T2 - 2016 8th International Conference on Computer and Automation Engineering, ICCAE 2016
Y2 - 3 March 2016 through 4 March 2016
ER -