Detection of Musical Borrowing Using Data Science

被引：0

作者：

Walczak S. ^{[1
]}

Moore-Pizon T.E., Jr. ^{[2
]}

机构：

[1] University of South Florida, United States

[2] Keiser University, United States

来源：

Proceedings of the Association for Information Science and Technology | 2023年 / 60卷 / 01期

关键词：

Borrowing; copyright; data science; music; note patterns;

D O I：

10.1002/pra2.843

中图分类号：

学科分类号：

摘要：

Data science may be used to determine similarities between musical scores. Programs are written in C++ to capture note progressions from musical scores and to compare progressions from different songs to identify overlapping areas. These tools enable the study of musical borrowing across musical genres and may assist in copyright violation cases. Results indicate that within the Celtic music genre, borrowing occurs across greater than 10% of the songs. Annual Meeting of the Association for Information Science & Technology | Oct. 27 – 31, 2023 | London, United Kingdom. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.

引用

页码：702 / 706

页数：4

共 11 条

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