Author(s)

Wenna Wu ¹

Affiliation(s)

¹ College of Science, Chang'an University, Xi'an, Shaanxi, 710064, China

Corresponding Author

ABSTRACT

Music creation is affected by many factors, especially music and music artists created in the past. First of all, this paper constructs a multi-layer network based on a given data set to visualize the relationship among genres, artists, songs and features. After calculating the centrality score, the sum of the scores of all subsequent nodes is described recursively to describe the authority and influence of music artists. After correlation analysis, we find that there is almost no linear correlation between its features. Then, we use tSNE (random neighbor embedding) to reduce its feature space to three-dimensional space, and use Euclidean distance to measure similarity in this space, indicating that these artists and songs are distributed in this space, even after most people are undersampled.

KEYWORDS

CITE THIS PAPER

Wenna Wu, Analysis and Research on the influence of Music based on degree-centered European Spatial Distribution. Journal of Electronics and Information Science (2022) Vol. 7: 94-98. DOI: http://dx.doi.org/10.23977/jeis.2022.070116.

REFERENCES

[1] Han H, Wang W Y, Mao B H. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning [C] // Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I. 2005. 
[2] Liu X Y, Wu J, Zhou Z H. Exploratory Undersampling for Class-Imbalance Learning [J]. IEEE Transactions on Systems Man & Cybernetics Part B, 2009, 39(2): 539-550. 
[3] Matthias, Mauch, Robert, et al. The evolution of popular music: USA 1960–2010 [J]. Open Science, 2015. 
[4] Fitch W T. The evolution of music in comparative perspective. [J]. Annals of the New York Academy of Sciences, 2005, 1060 (Dec): 29.

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