A Model for Music Influence, Similarity and Evolution Analysis Based on Directed Network
Authors: 
Xinran Tang, Yanxi Lin, Mingdong HeAuthors Info & Claims
Xinran Tang, Yanxi Lin, Mingdong HeAuthors Info & ClaimsPages 128 - 134
Abstract
Music is developed under the mutual influence of artists from various genres. We develop a model based on some advanced algorithms to excavate artists and genres' significant evolutionary and revolutionary trends. Firstly, we build a network model and analyze the modularity, density, and average degree, representing the influence level of certain artists. Next, we expand our model and establish a classification system using a hierarchical clustering model to analyze the similarities and differences among those music genres. Then, we could combine our quantitative analysis with the qualitative analysis by studying the literature background such as social, political, cultural, and technological factors along with the music genres evolution. The experimental results using the ICM Society data set show that our model's musical influence, similarity, and evolution are consistent with the actual situation.
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Published In
November 2021
148 pages
ISBN:9781450387194
DOI:10.1145/3507524
Copyright © 2021 ACM.
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Association for Computing Machinery
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Published: 08 March 2022
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ICCBD 2021
ICCBD 2021: 2021 4th International Conference on Computing and Big Data
November 27 - 29, 2021
Wuhan, China
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