LYRICS-BASED MUSIC GENRE CLASSIFICATION USING MACHINE LEARNING ALGORITHMS

  • In recent years, music genre classification has been studied widely within the musical information retrieval community to detect music genre (e.g., pop, rap) automatically. The existing methods, reported in the literature, usually extract features from the melodic content or lyrics of the song and address this classification as a multi-class problem. This thesis presents a comprehensive investigation of the prediction of the music genre solely from an examination of the lyrical content of the songs. The lyrics were thoroughly analyzed to obtain the features as inputs to the various machine learning algorithms and the features were represented using tf-idf values. In order to perform the algorithms, a dataset with a total of 12,000 songs for 12 genres was created by crawling the music websites. Furthermore, this study considers the genre classification as a multi-label task in which a song can belong to more than one genre as it is encountered. Therefore, multi-label approaches were examined in-depth and applied along with the popular classifiers. The experiments in this thesis show that binary relevance method conducted with logistic regression model outperforms among all for the lyric-based multi-label genre classification.

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Metadaten
Author:Neslihan Neslihan
Referee:Markus Löcher
Advisor:Roland Mueller
Document Type:Master's Thesis
Language:English
Date of first Publication:2019/12/19
Publishing Institution:Hochschulbibliothek HWR Berlin
Granting Institution:Hochschule für Wirtschaft und Recht Berlin
Date of final exam:2019/07/22
Release Date:2019/12/19
Tag:lyrics analysis; multi-label classification; music genre classification; music information retrieval; text mining
Page Number:85
Institutes:FB I - Wirtschaftswissenschaften / Business Intelligence and Process Management M.Sc.
Licence (German):License LogoUrheberrechtsschutz