Arabic Music Genre Identification

Moataz Ahmed, Sherif Fadel, Manal E. Helal, Abdel Moneim Wahdan

Research output: Contribution to journalReview articlepeer-review

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.
Original languageEnglish
Pages (from-to)187–200
Number of pages14
JournalJournal of Advanced Research in Applied Sciences and Engineering Technology
Volume46
Issue number1
Early online date22 May 2024
DOIs
Publication statusE-pub ahead of print - 22 May 2024

Keywords

  • Music Information Retrieval (MIR)
  • Genre/Maqam Classification
  • Machine Learning

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