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  4. Vol 2, Issue 5, 2021

Research on the Evolution of Music Schools

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DOI: 10.23977/artpl.2021.020509 | Downloads: 2 | Views: 715

Author(s)

Chengsi Wang 1, Jiusi Yu 2

Affiliation(s)

1 School of Statistics, Beijing Normal University, Beijing, 100875
2 Department of Physics, Beijing Normal University, Beijing, 100875

Corresponding Author

Chengsi Wang

ABSTRACT

In order to understand the music more deeply, we need to develop a way to quantify the evolution of music. To solve the problems mentioned above, this paper classifies and weights the twelve features through cluster analysis, and establishes two model development parameters for judging music influence and music similarity to describe music influence. Reduce the five-dimensional parameters to the point that each artist was a five-dimensional vector. The artist style of a particular year is analyzed, and the measurement criteria are developed to define revolutionaries in two ways.

KEYWORDS

Cluster analysis, Model for music similarity evaluation, Genre differences

CITE THIS PAPER

Chengsi Wang, Jiusi Yu. Research on the Evolution of Music Schools. Art and Performance Letters (2021) 2: 43-46. DOI: http://dx.doi.org/10.23977/artpl.2021.020509.

REFERENCES

[1] Zhou Benjin, Tao Yizheng, Ji Bin, et al. K-means initial clustering center optimization method to minimize the error square sum [J]. Computer Engineering and Applications, 2018, 54(15): 48-52.
[2] http://dict.youdao.com/w/comedy.
[3] Wang Wei, Wang Hongwei, Meng Yuan. Collaborative filtering recommendation algorithm research: Considering online review sentiment tendency [J]. System engineering theory and practice, 2014,34 (12): 3238-3249.

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