Author(s)

Qianqian Xu ¹

Affiliation(s)

¹ School of Management, Hainan University, Haikou, Hainan, 570228

Corresponding Author

Qianqian Xu

ABSTRACT

Music evolves among the development of human society. In this paper, we first transformed the influencing information to a reflection association mapping network and determined two basic indicators of the network. Then we established a TOPSIS music influence evaluation model based on the entropy method for the sub-network. We compared influencers’ influences respectively and revealed people’s preference for music characteristics of the era. Second, we provided methods to measure music similarity. Dimensionality of music feature indicators are reduced with PCA. Then cosine similarity method was used to establish a multi-index music similarity measurement model. To compare the similarity of artists within and between genres, K-means cluster analysis was used to re-divide artists into 19 music styles we did cross comparison with the original genres and compared different artists within the genre. It leads to the conclusion that artists within genres are more similar than artists between genres.

KEYWORDS

Graph association network, Music characteristics, K-means cluster analysis

CITE THIS PAPER

Qianqian Xu. Research on Evaluation of Music-about Artist Influence, Genres and Characteristics. Art and Performance Letters (2021) 2: 52-57. DOI: http://dx.doi.org/10.23977/artpl.2021.22009.

REFERENCES

[1] Orpen K S, Huron D. Measurement of Similarity in Music: A Quantitative Approach for Non-parametric Representations Similarity: Qualitative and Quantitative Aspects [J]. Computers in Music Research, 1992.

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