Issue |
MATEC Web of Conferences
Volume 56, 2016
2016 8th International Conference on Computer and Automation Engineering (ICCAE 2016)
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Article Number | 03001 | |
Number of page(s) | 8 | |
Section | Signal Analysis and Processing Technology | |
DOI | https://doi.org/10.1051/matecconf/20165603001 | |
Published online | 26 April 2016 |
Based on Gaussian Mixture Model to Explore the General Characteristic and Make Recognition of XinTianYou
1 Centre for Music Education, Xi’an Jiaotong University, 710049 Xi’an, China
2 Department of Computer Science & Technology, Xi’an Jiaotong University, 710049 Xi’an, China
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
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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