Comparative Analysis of SVM, k-NN and Logistic Regression Methods in Classifying Turkish Music Genres

Mehmet Cüneyt Özbalcı; Turgay Tugay Bilgin

doi:10.38088/jise.1809289

Comparative Analysis of SVM, k-NN and Logistic Regression Methods in Classifying Turkish Music Genres

Abstract

Music genre classification represents a fundamental challenge within the field of Music Information Retrieval (MIR). The analysis of audio signals plays a pivotal role in the process of music genre classification, facilitating the extraction of pertinent information from the frequency-based data of the auditory content. In this study, diverse acoustic characteristics were derived through the utilization of the librosa library, and subsequent classification procedures were executed employing machine learning algorithms. For the purpose of this study, a dataset comprising a total of 600 music files in WAV format was meticulously curated. This dataset encompassed six distinct genres, all rooted in Turkish musical traditions. Subsequently, classification tasks were undertaken using Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), and Logistic Regression algorithms. A series of experiments was conducted, varying the kernel functions and distance metrics employed. The findings of this investigation reveal the highest achieved accuracy rates, which amounted to 71.88% with k-NN, 73.44% with Logistic Regression, and 78.65% with the SVM algorithm. Notably, the SVM algorithm demonstrated superior performance in comparison to all other methodologies explored in this study.

Keywords

Ethical Statement

This study was conducted in accordance with research and publication ethics. All data used in the research were obtained within legal and ethical frameworks. Since the study does not involve any human participants or animal experiments, an ethics committee approval was not required.

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Details

Primary Language

English

Subjects

Semi- and Unsupervised Learning

Journal Section

Research Article

Authors

Mehmet Cüneyt Özbalcı ^*
0000-0003-4499-0061
Türkiye

Turgay Tugay Bilgin
0000-0002-9245-5728
Türkiye

Publication Date

April 11, 2026

Submission Date

October 23, 2025

Acceptance Date

January 7, 2026

Published in Issue

Year 2026 Volume: 10 Number: 1

DOI

https://doi.org/10.38088/jise.1809289

IZ

https://izlik.org/JA82ZB75RM

Cite

RIS / Bibtex

APA

Özbalcı, M. C., & Bilgin, T. T. (2026). Comparative Analysis of SVM, k-NN and Logistic Regression Methods in Classifying Turkish Music Genres. Journal of Innovative Science and Engineering, 10(1), 172-187. https://doi.org/10.38088/jise.1809289

AMA

1.Özbalcı MC, Bilgin TT. Comparative Analysis of SVM, k-NN and Logistic Regression Methods in Classifying Turkish Music Genres. JISE. 2026;10(1):172-187. doi:10.38088/jise.1809289

Chicago

Özbalcı, Mehmet Cüneyt, and Turgay Tugay Bilgin. 2026. “Comparative Analysis of SVM, K-NN and Logistic Regression Methods in Classifying Turkish Music Genres”. Journal of Innovative Science and Engineering 10 (1): 172-87. https://doi.org/10.38088/jise.1809289.

EndNote

Özbalcı MC, Bilgin TT (April 1, 2026) Comparative Analysis of SVM, k-NN and Logistic Regression Methods in Classifying Turkish Music Genres. Journal of Innovative Science and Engineering 10 1 172–187.

IEEE

[1]M. C. Özbalcı and T. T. Bilgin, “Comparative Analysis of SVM, k-NN and Logistic Regression Methods in Classifying Turkish Music Genres”, JISE, vol. 10, no. 1, pp. 172–187, Apr. 2026, doi: 10.38088/jise.1809289.

ISNAD

Özbalcı, Mehmet Cüneyt - Bilgin, Turgay Tugay. “Comparative Analysis of SVM, K-NN and Logistic Regression Methods in Classifying Turkish Music Genres”. Journal of Innovative Science and Engineering 10/1 (April 1, 2026): 172-187. https://doi.org/10.38088/jise.1809289.

JAMA

1.Özbalcı MC, Bilgin TT. Comparative Analysis of SVM, k-NN and Logistic Regression Methods in Classifying Turkish Music Genres. JISE. 2026;10:172–187.

MLA

Özbalcı, Mehmet Cüneyt, and Turgay Tugay Bilgin. “Comparative Analysis of SVM, K-NN and Logistic Regression Methods in Classifying Turkish Music Genres”. Journal of Innovative Science and Engineering, vol. 10, no. 1, Apr. 2026, pp. 172-87, doi:10.38088/jise.1809289.

Vancouver

1.Mehmet Cüneyt Özbalcı, Turgay Tugay Bilgin. Comparative Analysis of SVM, k-NN and Logistic Regression Methods in Classifying Turkish Music Genres. JISE. 2026 Apr. 1;10(1):172-87. doi:10.38088/jise.1809289