Arabic Music Classification Using Machine Learning Algorithms with Combined Robust Features

M. E. ElAlami¹, S. M. K. Tobar², S. M. Khater¹, Eman A. Esmaeil^1,*

¹Computer Science Department, Faculty of Specific Education, Mansoura University, Mansoura, Egypt

²Musical Education Department, Faculty of Specific Education, Mansoura University, Mansoura, Egypt

Emails: moh_elalmi@mans.edu.eg; sahartobar@mans.edu.eg; shim_khater@mans.edu.eg; emanatya92@mans.edu.eg

Abstract

Machine learning (ML) is the most up-to-date approach for classifying music genres. Due to technological ML advancements, its technologies can help in music genre recognition best. In machine learning, effective fusion of different features could improve recognition performance. Hence, this paper presents a new robust method for Arabic music classification based on the fusion of different sets of features. Frequency-domain, time-domain, and cepstral domain features have been combined and compared with other state-of-the-art approaches. Four machine-learning models that categorize music into its appropriate genre have been created: support vector machines (SVM), K-nearest neighbors (KNN), naïve Bayes (NB), and random forest (RF) classifiers were utilized in a comparative analysis of other ML algorithms, and the accuracy of these models has been assessed and derives the appropriate conclusions. To assess the performance of our method, two various datasets are used: the collected dataset, namely Zekrayati, which was collected by authors in favor of this paper, and the global GTZAN dataset, which was used to compare with previous studies. The experimental findings indicated that the SVM exhibited a higher optimal accuracy of 99.2% and has proven that the fusion proposed features will help to classify music in different fields.

Keywords:  Machine learning (ML); Musical classification; Music information retrieval (MIR)