Music similarity models applied to cover song identification and classification

The purpose of this study: 1) To find out copyright violations in the field of music in the form of a cover song by recording through social media connected with Law Number 28 of 2014 concerning Copyright. 2) To find out the factors that influence copyright violations in the music field in the form of cover songs by recording through social media. The writing of this thesis uses a statute approach, by reviewing amendments to Law Number 28 of 2014 concerning Copyright and its implications for copyright and its implementation by state institutions and the Republic of Indonesia Law No. 19 of 2016 concerning Amendment of Law Number 11 Year 2008 Regarding Information and Electronic Transactions. Results of research conducted by the author: When someone does a cover song through social media without permission from the creator, both for the purpose not for commercial or commercial purposes, it is an infringement of copyright. Factors that influence the occurrence of violations of copyright in Indonesia include: 1) Weak law enforcement against violators. 2) Works on the internet can easily be duplicated and disseminated globally in a short period of time and in large quantities. 3) There is no limit on the place of the offender because a domain name or website can be accessed by anyone globally. 4) Procedures for events between countries in dealing with violations of copyright on the internet, such as to determine who the perpetrators are and when they occur and determine the jurisdiction of violations still vary.

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Content-based music similarity computation with relevant component analysis

2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) ◽

10.1109/cisp-bmei.2016.7852867 ◽

2016 ◽

Cited By ~ 1

Author(s):

Wei Du ◽

Hu Lin ◽

Jianwei Sun ◽

Bo Yu ◽

Haibo Yang

Keyword(s):

Component Analysis ◽

Music Similarity ◽

Similarity Computation ◽

Relevant Component

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Quantitative Analysis of Comprehensive Influence of Music Network Based on Logistic Regression and Bidirectional Clustering

Complexity ◽

10.1155/2021/2996750 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Yi-Kun Zhao ◽

Guo-Qing Wang ◽

Xiao-Xiao Zhan ◽

Peng-Hui Yang

Keyword(s):

Logistic Regression ◽

Quantitative Analysis ◽

Comprehensive Evaluation ◽

Dynamic Network ◽

Measurement Model ◽

Music Similarity ◽

Pagerank Algorithm ◽

Change Factors ◽

Cluster Analysis Method ◽

The Impact

This paper makes a quantitative analysis of the comprehensive influence of music networks. Firstly, 11 music features are selected from energy, popularity, and other aspects to build a comprehensive evaluation index of music influence, and the PageRank algorithm is used to quantify the music influence. Secondly, the multiobjective logistic regression is used to construct the music similarity measurement model and, combined with music influence and music similarity, to judge whether the influence of different musicians is the actual influence. Thirdly, the influence and similarity of the same music genre and different music genres are analyzed by using the two-way cluster analysis method. Finally, the lasso region is used for feature selection to obtain the change factors in the process of music evolution and analyze the dynamic changes in the process of music development. Therefore, this paper uses network science to build a dynamic network to analyze the similarity of music, the evolution process, and the impact of music on culture, which has certain research significance and practical value in the fields of music, history, social science, and practice.

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