Development of Quran reciter identification system using MFCC and neural network
Currently, the Quran is recited by so many reciters with different ways and voices. Some people like to listen to this reciter and others like to listen to other reciters. Sometimes we hear a very nice recitation of al-Quran and want to know who the reciter is. Therefore, this paper is about the d...
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| Format: | Article |
| Language: | English English |
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Institute of Advanced engineering and Science (IAES)
2016
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| Online Access: | http://irep.iium.edu.my/51467/ http://irep.iium.edu.my/51467/1/18_updated16_16Nov15_9114_Development_of_Quran_Reciter.pdf http://irep.iium.edu.my/51467/4/51467_Development%20of%20Quran%20reciter%20_Scopus.pdf |
| _version_ | 1848783857474600960 |
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| author | Asda, Tayseer Mohammed Hasan Gunawan, Teddy Surya Kartiwi, Mira Mansor, Hasmah |
| author_facet | Asda, Tayseer Mohammed Hasan Gunawan, Teddy Surya Kartiwi, Mira Mansor, Hasmah |
| author_sort | Asda, Tayseer Mohammed Hasan |
| building | IIUM Repository |
| collection | Online Access |
| description | Currently, the Quran is recited by so many reciters with different ways and voices. Some people like to listen to this reciter and others like to listen to other reciters. Sometimes we hear a very nice recitation of al-Quran and want to know who the reciter is. Therefore, this paper is about the development of Quran reciter recognition and identification system based on mel frequency cepstral coefficient (MFCC) feature extraction and artificial neural network (ANN). From every speech, characteristics from the utterances will be extracted through neural network model. In this paper a database of five Quran reciters is created and used in training and testing. The feature vector will be fed into neural network back propagation learning algorithm for training and identification processes of different speakers. Consequently, 91.2% of the successful match between targets and input occurred with certain number of hidden layers which shows how efficient are mel frequency cepstral coefficient (MFCC) feature extraction and artificial neural network (ANN) in identifying the reciter voice perfectly. |
| first_indexed | 2025-11-14T16:28:01Z |
| format | Article |
| id | iium-51467 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T16:28:01Z |
| publishDate | 2016 |
| publisher | Institute of Advanced engineering and Science (IAES) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-514672017-04-13T04:08:14Z http://irep.iium.edu.my/51467/ Development of Quran reciter identification system using MFCC and neural network Asda, Tayseer Mohammed Hasan Gunawan, Teddy Surya Kartiwi, Mira Mansor, Hasmah TK Electrical engineering. Electronics Nuclear engineering Currently, the Quran is recited by so many reciters with different ways and voices. Some people like to listen to this reciter and others like to listen to other reciters. Sometimes we hear a very nice recitation of al-Quran and want to know who the reciter is. Therefore, this paper is about the development of Quran reciter recognition and identification system based on mel frequency cepstral coefficient (MFCC) feature extraction and artificial neural network (ANN). From every speech, characteristics from the utterances will be extracted through neural network model. In this paper a database of five Quran reciters is created and used in training and testing. The feature vector will be fed into neural network back propagation learning algorithm for training and identification processes of different speakers. Consequently, 91.2% of the successful match between targets and input occurred with certain number of hidden layers which shows how efficient are mel frequency cepstral coefficient (MFCC) feature extraction and artificial neural network (ANN) in identifying the reciter voice perfectly. Institute of Advanced engineering and Science (IAES) 2016-01 Article PeerReviewed application/pdf en http://irep.iium.edu.my/51467/1/18_updated16_16Nov15_9114_Development_of_Quran_Reciter.pdf application/pdf en http://irep.iium.edu.my/51467/4/51467_Development%20of%20Quran%20reciter%20_Scopus.pdf Asda, Tayseer Mohammed Hasan and Gunawan, Teddy Surya and Kartiwi, Mira and Mansor, Hasmah (2016) Development of Quran reciter identification system using MFCC and neural network. TELKOMNIKA Indonesian Journal of Electrical Engineering, 17 (1). pp. 168-175. ISSN 2302-4046 http://www.iaescore.com/journals/index.php/IJEECS/article/view/196/59 10.11591/ijeecs.v1.i1.pp168-175 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Asda, Tayseer Mohammed Hasan Gunawan, Teddy Surya Kartiwi, Mira Mansor, Hasmah Development of Quran reciter identification system using MFCC and neural network |
| title | Development of Quran reciter identification system using MFCC and neural network |
| title_full | Development of Quran reciter identification system using MFCC and neural network |
| title_fullStr | Development of Quran reciter identification system using MFCC and neural network |
| title_full_unstemmed | Development of Quran reciter identification system using MFCC and neural network |
| title_short | Development of Quran reciter identification system using MFCC and neural network |
| title_sort | development of quran reciter identification system using mfcc and neural network |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://irep.iium.edu.my/51467/ http://irep.iium.edu.my/51467/ http://irep.iium.edu.my/51467/ http://irep.iium.edu.my/51467/1/18_updated16_16Nov15_9114_Development_of_Quran_Reciter.pdf http://irep.iium.edu.my/51467/4/51467_Development%20of%20Quran%20reciter%20_Scopus.pdf |