Author(s)

Zhiqun Li ¹, Lusheng Wang ², Dandan Wang ³

Affiliation(s)

¹ School of Business Administration, Harbin University of Commerce, Harbin, Heilongjiang 150028
² School of Energy and Civil Engineering, Harbin University of Commerce, Harbin, Heilongjiang 150028
³ Finance School, Harbin University of Commerce, Harbin, Heilongjiang 150028

Corresponding Author

Zhiqun Li

ABSTRACT

In order to understand the role of music in our human lives, it is very important to develop a method that can quantify the evolution of music. First, we selected seven indicators of music characteristics, used Pearson's correlation coefficient to construct a music similarity measurement model, and performed a cluster analysis of artists within the genre to solve the Pearson correlation coefficient within and between genres. It is concluded that the music similarity within genres is higher than that between genres. Next, we selected some indicators through principal component analysis, observed the change curve of indicators over time, and analyzed how the genre changes over time. Finally, we use the coefficient of variation method to analyze the musical characteristics of influencers and followers, and obtain the relationship between the appeal of musical characteristics.

KEYWORDS

K-means, Pearson correlation coefficient, Coefficient of Variation, music

CITE THIS PAPER

Zhiqun Li, Lusheng Wang, Dandan Wang. Analysis of music similarity based on Pearson correlation coefficient. Art and Performance Letters (2021) 2: 47-51. DOI: http://dx.doi.org/10.23977/artpl.2021.020510.

REFERENCES

[1] ZHANG Shiqiang, LÜ Jieneng, JIANG Zheng, et al. Study of the correlation coefficients in mathematical statistics [J]. Mathematics in Practice and Theory, 2009, 39(19): 102-107. 
[2] SHAO Fan, CHEN Chen, GE Miaojia, et al. Analysis of technology innovation and application based on the power line loss Pearson algorithm [J]. Scientific and TechnologicalInnovation, 2017(14): 54-55.
[3] Jain A K. Data clustering: 50 years beyond K-means [J]. Pattern Recognition Letters, 2010, 31(8): 651-666.

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