Neural Networks-Based Automatic Audio Classification for Al-Quran Chapters

Al-Quran Audio classification is one example of content-based analysis of audio signals. This study aims to design a neural network that is able to classify Al-Quran audio files to the correct chapter [special characters omitted] and this requires implementing state of the art Convolutional Neural Network (CNN) to train Al-Quran Dataset and predict the correct chapter (سورة ). In order to achieve this aim, a critical evaluation of the current state of the automatic based reciting classification of Al-Quran was conducted, and the principles, assumptions and methods at the field were used to present a prototype based on this evaluation. Special focus is placed upon creating a suitable robust Quranic dataset and on discovering the features of that dataset that make it possible for an automated recognition of Al-Quran chapters and recitation. In addition, it sets out principles that should be kept in mind when designing Al-Quran reciting recognition and learning systems, and a prototype based on these features is presented. The thesis provides a framework for the auditory classification of Al-Quran chapters, as the final results shows that the use of a newly created IQRA-15 dataset and CNN as a model architecture produced in excess of 90% accuracy on unseen data. This is a proof of concept that deep learning can achieve good results when applied to Al-Quran. This knowledge can be used to design an AI based system for self-correcting Al-Quran recitation for Arabs and non-native Arabic speakers.