On the Use of Feature Selection for Music Genre Classification

AL TAMIMI, Abdel-Karim, SALEM, Maher and AL-ALAMI, Ahmad (2020). On the Use of Feature Selection for Music Genre Classification. In: 2020 Seventh International Conference on Information Technology Trends (ITT). Piscataway, New Jersey, IEEE, 1-6.

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Official URL: https://ieeexplore.ieee.org/document/9320778
Link to published version:: https://doi.org/10.1109/itt51279.2020.9320778


In the recent years, utilizing machine learning in musicrelated problems has attracted researchers in both industry and academia. One of the recent targeted challenges is classifying music segments based on their genre, which is done according to the extracted features of their audio tracks. This identification process plays a major rule in the user-tailored recommendation systems employed by the widely used web services like Spotify and YouTube. In this paper, we demonstrate the use of feature selection combined with Support Vector Machine (SVM) classifier to classify the recently shared open-source FMA (Free Music Archive) dataset. We use information-gain feature selection method to select the minimum number of features required for classification without affecting the accuracy of the model. We demonstrate that confining the model to use the top selected features have reduced the model complexity, and significantly reduced the processing time without sacrificing accuracy.

Item Type: Book Section
Additional Information: 2020 Seventh International Conference on Information Technology Trends (ITT), 25-26 November 2020 © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identification Number: https://doi.org/10.1109/itt51279.2020.9320778
Page Range: 1-6
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 26 Apr 2023 14:44
Last Modified: 11 Oct 2023 15:45
URI: https://shura.shu.ac.uk/id/eprint/31046

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