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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2023/2642558 |
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| author | Ghassan Samara Essam Al-Daoud Nael Swerki Dalia Alzu’bi |
| author_facet | Ghassan Samara Essam Al-Daoud Nael Swerki Dalia Alzu’bi |
| author_sort | Ghassan Samara |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-2d7ca9486a8b4962aaa1abe84302d97c |
| institution | Kabale University |
| issn | 1687-5699 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-2d7ca9486a8b4962aaa1abe84302d97c2025-02-03T06:42:41ZengWileyAdvances in Multimedia1687-56992023-01-01202310.1155/2023/2642558The Recognition of Holy Qur’an Reciters Using the MFCCs’ Technique and Deep LearningGhassan Samara0Essam Al-Daoud1Nael Swerki2Dalia Alzu’bi3Department of Computer ScienceDepartment of Computer ScienceDepartment of Computer ScienceDepartment of Computer Information SystemsThe 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%.http://dx.doi.org/10.1155/2023/2642558 |
| spellingShingle | Ghassan Samara Essam Al-Daoud Nael Swerki Dalia Alzu’bi The Recognition of Holy Qur’an Reciters Using the MFCCs’ Technique and Deep Learning Advances in Multimedia |
| title | The Recognition of Holy Qur’an Reciters Using the MFCCs’ Technique and Deep Learning |
| title_full | The Recognition of Holy Qur’an Reciters Using the MFCCs’ Technique and Deep Learning |
| title_fullStr | The Recognition of Holy Qur’an Reciters Using the MFCCs’ Technique and Deep Learning |
| title_full_unstemmed | The Recognition of Holy Qur’an Reciters Using the MFCCs’ Technique and Deep Learning |
| title_short | The Recognition of Holy Qur’an Reciters Using the MFCCs’ Technique and Deep Learning |
| title_sort | recognition of holy qur an reciters using the mfccs technique and deep learning |
| url | http://dx.doi.org/10.1155/2023/2642558 |
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