The Development Trend of Musicians’ Influence and Music Genres of Big Data
This paper uses the data crawled from the AllMusic website to establish a directional network of followers and influences of music genre artists, analyzes the music influence influenced by genre. The Beatles had the greatest influence from 1950 to 2010, and promoted the development of Pop/Rock and C...
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EDP Sciences
2021-01-01
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doaj-2cab58d723a144eb8038ab17542e42e82021-05-28T12:35:19ZengEDP SciencesE3S Web of Conferences2267-12422021-01-012530208510.1051/e3sconf/202125302085e3sconf_eem2021_02085The Development Trend of Musicians’ Influence and Music Genres of Big DataXi chun Zhang0Li He1Jing yi Wang2Wei Liu3SETTING: Shan Dong Normal UniversitySETTING: Shan Dong Normal UniversitySETTING: Shan Dong Normal UniversitySETTING: Shan Dong Normal UniversityThis paper uses the data crawled from the AllMusic website to establish a directional network of followers and influences of music genre artists, analyzes the music influence influenced by genre. The Beatles had the greatest influence from 1950 to 2010, and promoted the development of Pop/Rock and Country music genres. In addition, it was found that “influencers” would actually influence the music created by followers. Based on the music feature data set of 91719 songs provided by Spotify’s API, drawing the correlation heat map and making the measurement of music similarity, it is found that the songs of artists of the same genre are more similar. For the similarity between different genres, by selecting the representative music in the genre and using the music characteristics to analyze their correlation, it is found that Folk and Avant-Garde, New Age and Stage & Screen all have high similarity, reaching 0.97. In addition, songs can also be classified into genres according to music characteristics. For example, if a genre has high performance in livability, speech and explicit attributes, it can be considered as Comedy/Spoken. Finally, combined with the historical reality, it is found that there may be characteristics and music revolutionaries[1] that mark the great revolution of music development.https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/29/e3sconf_eem2021_02085.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xi chun Zhang Li He Jing yi Wang Wei Liu |
spellingShingle |
Xi chun Zhang Li He Jing yi Wang Wei Liu The Development Trend of Musicians’ Influence and Music Genres of Big Data E3S Web of Conferences |
author_facet |
Xi chun Zhang Li He Jing yi Wang Wei Liu |
author_sort |
Xi chun Zhang |
title |
The Development Trend of Musicians’ Influence and Music Genres of Big Data |
title_short |
The Development Trend of Musicians’ Influence and Music Genres of Big Data |
title_full |
The Development Trend of Musicians’ Influence and Music Genres of Big Data |
title_fullStr |
The Development Trend of Musicians’ Influence and Music Genres of Big Data |
title_full_unstemmed |
The Development Trend of Musicians’ Influence and Music Genres of Big Data |
title_sort |
development trend of musicians’ influence and music genres of big data |
publisher |
EDP Sciences |
series |
E3S Web of Conferences |
issn |
2267-1242 |
publishDate |
2021-01-01 |
description |
This paper uses the data crawled from the AllMusic website to establish a directional network of followers and influences of music genre artists, analyzes the music influence influenced by genre. The Beatles had the greatest influence from 1950 to 2010, and promoted the development of Pop/Rock and Country music genres. In addition, it was found that “influencers” would actually influence the music created by followers. Based on the music feature data set of 91719 songs provided by Spotify’s API, drawing the correlation heat map and making the measurement of music similarity, it is found that the songs of artists of the same genre are more similar. For the similarity between different genres, by selecting the representative music in the genre and using the music characteristics to analyze their correlation, it is found that Folk and Avant-Garde, New Age and Stage & Screen all have high similarity, reaching 0.97. In addition, songs can also be classified into genres according to music characteristics. For example, if a genre has high performance in livability, speech and explicit attributes, it can be considered as Comedy/Spoken. Finally, combined with the historical reality, it is found that there may be characteristics and music revolutionaries[1] that mark the great revolution of music development. |
url |
https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/29/e3sconf_eem2021_02085.pdf |
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