Title: Intelligent classification model for holy Quran recitation Maqams

Authors: Aaron Rasheed Rababaah

Addresses: College of Engineering and Applied Sciences, American University of Kuwait, Salmiya, Kuwait

Abstract: Quranic recitation is a field that has been studied for centuries by scholars from different disciplines including tajweed scholars, musicians and historians. Maqams are a system of scales of melodic vocal patterns that have been established and practiced by Quran reciters all over the world for centuries. Traditionally, Maqams are taught by an expert of Quran recitation. We are proposing a process model for intelligent classification of Quran maqams using a comparative study of neural networks, deep learning and clustering techniques. We utilised a publicly available audio dataset of Maqams labelled audio signals consisting of the eight primary Maqams: Ajam, Bayat, Hijaz, Kurd, Nahawand, Rast, Saba, and Seka. The experimental work showed that all of the three classifiers, nearest neighbour, multi-layered perceptron and deep learning performed well. Furthermore, it was found that deep learning with power spectrum features was the best model with a classification accuracy of 96.55%.

Keywords: Quran Maqams; neural networks; signal processing; deep learning; convolutional neural networks; CNN; audio signal features; short-term Fourier transform; STFT; power spectrum.

DOI: 10.1504/IJCVR.2024.136995

International Journal of Computational Vision and Robotics, 2024 Vol.14 No.2, pp.170 - 190

Received: 09 Jul 2022
Accepted: 02 Aug 2022
Published online: 01 Mar 2024 *

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