Subgenre classification in hip hop music an analysis of machine learning architectures

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dc.contributor.authorPaşa, Can
dc.contributor.authorTarikci, Abdurrahman
dc.date.accessioned2026-04-04T13:19:03Z
dc.date.available2026-04-04T13:19:03Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractDigitalisation and the proliferation of online music listening platforms have led to the exponential growth of music data on the Internet, thus necessitating the development of automated systems for data organisation and analysis. In this context, automatic genre classification practices have become a significant approach for the efficiency of music discovery and recommendation processes. While significant progress has been made in genre classification, subgenre classification remains an under-researched area, despite its potential to provide more personalised listening experiences. This study aims to address this gap by focusing on the classification of hip-hop music subgenres, namely boombap, jazzrap and trap, utilising a comprehensive dataset comprising 750 audio files. The study extracts a total of 31 features, encompassing both spectral and psychoacoustic characteristics. Machine learning models such as Logistic Regression, K-Nearest Neighbours, Decision Tree and Random Forest are employed, along with the Artificial Neural Network, which attains the highest accuracy of 85%. The findings reveal that subgenre classification poses challenges, especially for categories such as jazzrap and boombap, which share overlapping musical characteristics. In contrast, trap with different timbral characteristics was classified with higher accuracy. This study contributes to the scant research on subgenre classification by underscoring the viability of employing deep learning techniques to enhance the precision of comprehensive datasets and intricate subgenre categorisations. Moreover, this research underscores the pivotal role of subgenre classification within the ambit of digital music platforms. The accurate identification of subgenres not only elevates the overall auditory experience for users but also facilitates the discovery of music selections that resonate closely with their individual preferences. © 2025 The Author(s).
dc.identifier.doi10.31811/ojomus.1624182
dc.identifier.endpage247
dc.identifier.issn2536-4421
dc.identifier.issue2
dc.identifier.scopus2-s2.0-105005106501
dc.identifier.scopusqualityQ4
dc.identifier.startpage235
dc.identifier.urihttps://doi.org/10.31811/ojomus.1624182
dc.identifier.urihttps://hdl.handle.net/11616/108125
dc.identifier.volume10
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherNilgun SAZAK
dc.relation.ispartofOnline Journal of Music Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_Scopus_20250329
dc.subjectdeep learning
dc.subjectmachine learning
dc.subjectmusic genre classification
dc.subjectmusic information retrieval
dc.subjectmusic subgenre classification
dc.titleSubgenre classification in hip hop music an analysis of machine learning architectures
dc.title.alternativeHip hop müzikte alt tür sınıflandırması: Makine öğrenimi mimarilerinin analizi]
dc.typeArticle

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