Arabic Music Genre Identification

Authors

  • Moataz Ahmed Computer Engineering Department, Arab Academy for Science, Technology and Maritime Transport, Cairo Governorate 4471344, Egypt
  • Sherif Fadel Computer Engineering Department, Arab Academy for Science, Technology and Maritime Transport, Cairo Governorate 4471344, Egypt
  • Manal Helal School of Physics, Engineering & Computer Science, University of Hertfordshire, Hatfield AL 10 9AB, United Kingdom
  • Abdel Moneim Wahdan Computer and System Department, Ain Shams University, Cairo Governate, Egypt

DOI:

https://doi.org/10.37934/araset.46.1.187200

Keywords:

MIR, Genre, Maqam classification, Machine learning

Abstract

Music Information Retrieval (MIR) is one data science application crucial for different tasks such as recommendation systems, genre identification, fingerprinting, and novelty assessment. Different Machine Learning techniques are utilised to analyse digital music records, such as clustering, classification, similarity scoring, and identifying various properties for the different tasks. Music is represented digitally using diverse transformations and is clustered and classified successfully for Western Music. However, Eastern Music poses a challenge, and some techniques have achieved success in clustering and classifying Turkish and Persian Music. This research presents an evaluation of machine learning algorithms' performance on pre-labelled Arabic Music with their Arabic genre (Maqam). The study introduced new data representations of the Arabic music dataset and identified the most suitable machine-learning methods and future enhancements.

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Author Biographies

Moataz Ahmed, Computer Engineering Department, Arab Academy for Science, Technology and Maritime Transport, Cairo Governorate 4471344, Egypt

moataz.amahmood@gmail.com

Sherif Fadel, Computer Engineering Department, Arab Academy for Science, Technology and Maritime Transport, Cairo Governorate 4471344, Egypt

fahmy@aast.edu

Manal Helal, School of Physics, Engineering & Computer Science, University of Hertfordshire, Hatfield AL 10 9AB, United Kingdom

m.helal@herts.ac.uk

Abdel Moneim Wahdan, Computer and System Department, Ain Shams University, Cairo Governate, Egypt

wahdan47@gmail.com

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Published

2024-06-04

How to Cite

Moataz Ahmed, Sherif Fadel, Manal Helal, & Abdel Moneim Wahdan. (2024). Arabic Music Genre Identification. Journal of Advanced Research in Applied Sciences and Engineering Technology, 46(1), 187–200. https://doi.org/10.37934/araset.46.1.187200

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Section

Articles