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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Main Authors:	Asda, Tayseer Mohammed Hasan, Gunawan, Teddy Surya, Kartiwi, Mira, Mansor, Hasmah
Format:	Article
Language:	English English
Published:	Institute of Advanced engineering and Science (IAES) 2016
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	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

id	iium-51467
recordtype	eprints
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
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TK Electrical engineering. Electronics Nuclear engineering
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
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.
format	Article
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
title	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_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_sort	development of quran reciter identification system using mfcc and neural network
publisher	Institute of Advanced engineering and Science (IAES)
publishDate	2016
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
first_indexed	2023-09-18T21:12:51Z
last_indexed	2023-09-18T21:12:51Z
_version_	1777411368084832256

Development of Quran reciter identification system using MFCC and neural network

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