Automatic Classification of Reciters of Quranic Audio Clips
Ashraf Elnagar, Rotana Ismail, Bahja Alattas, Alia Alfalasi · 2018
This paper describes a supervised classification system of Quranic audio clips of several reciters. The objective is to identify the reciter or the closest reciter to an input audio clip. It is common to find people who like to recite Quran mimicking one of the popular reciters. To achieve a practical classifier system, we constructed a representative dataset of audio clips for seven popular reciters from Saudi Arabia. Key features were extracted from the audio clips. We chose perceptual features such as pitch and tempo based features, short time energy etc. We have tried different combination of perceptual features in order to achieve better classification. We split the dataset into training and testing sets (80% & 20%, respectively). SVM is used to implement the classifier. Experimental results show that the proposed audio classifier produces promising results with an overall accuracy of 90%.