The Recognition of Holy Qur’an Reciters Using the MFCCs’ Technique and Deep Learning

The Holy Qur’an has recently gained recognition in the field of speech-processing research. It is the central book of Islam, from which Muslims derive their religious teachings. The Qur’an is the primary source and highest authority for all Islamic beliefs and legislation. It is also one of the most...

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Bibliographic Details
Published inAdvances in Multimedia Vol. 2023; pp. 1 - 14
Main Authors Samara, Ghassan, Al-Daoud, Essam, Swerki, Nael, Alzu’bi, Dalia
Format Journal Article
LanguageEnglish
Published New York Hindawi 21.03.2023
Hindawi Limited
Wiley
Subjects
Accuracy
Artificial neural networks
Audio data
Classification
Datasets
Deep learning
Islam
Legislation
Literature reviews
Machine learning
Muslims
Neural networks
Reading
Speech recognition
Support vector machines
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Summary:The Holy Qur’an has recently gained recognition in the field of speech-processing research. It is the central book of Islam, from which Muslims derive their religious teachings. The Qur’an is the primary source and highest authority for all Islamic beliefs and legislation. It is also one of the most widely memorized and recited texts around the world. Listening to and reciting the Qur’an is one of the most important daily practices for Muslims. In this study, we propose a deep learning model using convolutional neural networks (CNNs) and a dataset consisting of seven well-known reciters. We utilize mel frequency cepstral coefficients (MFCCs) to extract and evaluate information from audio sources. We compare our proposed model to different deep learning and machine learning methodologies. Our proposed model outperformed the competing models with an accuracy of 99.66%, compared to the support vector machine’s accuracy of 99%.
ISSN:1687-5680
1687-5699
DOI:10.1155/2023/2642558