Lyriccovers 2.0: An Enhanced Dataset for Cover Song Analysis

URN to cite this document:: urn:nbn:de:bvb:355-epub-752386
DOI to cite this document:: 10.5283/epub.75238

Balluff, Maximilian ; Auch, Maximilian

; Mandl, Peter

; Wolff, Christian

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Date of publication of this fulltext: 20 Mar 2025 17:40

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Item type:	Article
Open Access Type:	Unknown
Journal or Publication Title:	IADIS International Journal on WWW/Internet
Publisher:	IADIS
Volume:	22
Number of Issue or Book Chapter:	2
Page Range:	pp. 75-92
Date:	2024
Institutions:	Languages and Literatures > Institut für Information und Medien, Sprache und Kultur (I:IMSK) > Lehrstuhl für Medieninformatik (Prof. Dr. Christian Wolff) Informatics and Data Science > Department Human-Centered Computing > Lehrstuhl für Medieninformatik (Prof. Dr. Christian Wolff)
Keywords:	Cover Song Detection, Music Information Retrieval, Dataset, Lyrics
Dewey Decimal Classification:	000 Computer science, information & general works > 004 Computer science
Status:	Published
Refereed:	Yes, this version has been refereed
Created at the University of Regensburg:	Yes
Item ID:	75238

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Abstract

This research offers a detailed examination of a novel dataset that collates original musical compositions alongside their derivative cover versions. Unique in its inclusion of both links to YouTube as well as and lyrical content, the dataset enlists more than 78,000 tracks, encompassing more than 24,000 cover song groupings. It stands as the most diverse compendium of cover songs currently ...