Development of quranic reciter identification system using MFCC and GMM classifier

Nowadays, there are many beautiful recitation of Al-Quran available. Quranic recitation has its own characteristics, and the problem to identify the reciter is similar to the speaker recognition/identification problem. The objective of this paper is to develop Quran reciter identification system...

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Main Authors:	Gunawan, Teddy Surya, Muhamat Saleh, Nur Atikah, Kartiwi, Mira
Format:	Article
Language:	English English
Published:	Institute of Advanced Engineering and Science (IAES) 2018
Subjects:	T Technology (General) TK Electrical engineering. Electronics Nuclear engineering TS Manufactures
Online Access:	http://irep.iium.edu.my/64205/ http://irep.iium.edu.my/64205/ http://irep.iium.edu.my/64205/ http://irep.iium.edu.my/64205/1/64205_Development%20of%20Quranic%20Reciter%20Identification%20System_article.pdf http://irep.iium.edu.my/64205/2/64205_Development%20of%20Quranic%20Reciter%20Identification%20System_scopus.pdf

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spelling	iium-642052018-06-12T02:07:26Z http://irep.iium.edu.my/64205/ Development of quranic reciter identification system using MFCC and GMM classifier Gunawan, Teddy Surya Muhamat Saleh, Nur Atikah Kartiwi, Mira T Technology (General) TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Nowadays, there are many beautiful recitation of Al-Quran available. Quranic recitation has its own characteristics, and the problem to identify the reciter is similar to the speaker recognition/identification problem. The objective of this paper is to develop Quran reciter identification system using Mel-frequency Cepstral Coefficient (MFCC) and Gaussian Mixture Model (GMM). In this paper, a database of five Quranic reciters is developed and used in training and testing phases. We carefully randomized the database from various surah in the Quran so that the proposed system will not prone to the recited verses but only to the reciter. Around 15 Quranic audio samples from 5 reciters were collected and randomized, in which 10 samples were used for training the GMM and 5 samples were used for testing. Results showed that our proposed system has 100% recognition rate for the five reciters tested. Even when tested with unknown samples, the proposed system is able to reject it. Institute of Advanced Engineering and Science (IAES) 2018-02 Article PeerReviewed application/pdf en http://irep.iium.edu.my/64205/1/64205_Development%20of%20Quranic%20Reciter%20Identification%20System_article.pdf application/pdf en http://irep.iium.edu.my/64205/2/64205_Development%20of%20Quranic%20Reciter%20Identification%20System_scopus.pdf Gunawan, Teddy Surya and Muhamat Saleh, Nur Atikah and Kartiwi, Mira (2018) Development of quranic reciter identification system using MFCC and GMM classifier. International Journal of Electrical and Computer Engineering, 8 (1). pp. 372-378. ISSN 2088-8708 http://iaescore.com/journals/index.php/IJECE/article/view/9815/8181 10.11591/ijece.v8i1.pp372-378
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	T Technology (General) TK Electrical engineering. Electronics Nuclear engineering TS Manufactures
spellingShingle	T Technology (General) TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Gunawan, Teddy Surya Muhamat Saleh, Nur Atikah Kartiwi, Mira Development of quranic reciter identification system using MFCC and GMM classifier
description	Nowadays, there are many beautiful recitation of Al-Quran available. Quranic recitation has its own characteristics, and the problem to identify the reciter is similar to the speaker recognition/identification problem. The objective of this paper is to develop Quran reciter identification system using Mel-frequency Cepstral Coefficient (MFCC) and Gaussian Mixture Model (GMM). In this paper, a database of five Quranic reciters is developed and used in training and testing phases. We carefully randomized the database from various surah in the Quran so that the proposed system will not prone to the recited verses but only to the reciter. Around 15 Quranic audio samples from 5 reciters were collected and randomized, in which 10 samples were used for training the GMM and 5 samples were used for testing. Results showed that our proposed system has 100% recognition rate for the five reciters tested. Even when tested with unknown samples, the proposed system is able to reject it.
format	Article
author	Gunawan, Teddy Surya Muhamat Saleh, Nur Atikah Kartiwi, Mira
author_facet	Gunawan, Teddy Surya Muhamat Saleh, Nur Atikah Kartiwi, Mira
author_sort	Gunawan, Teddy Surya
title	Development of quranic reciter identification system using MFCC and GMM classifier
title_short	Development of quranic reciter identification system using MFCC and GMM classifier
title_full	Development of quranic reciter identification system using MFCC and GMM classifier
title_fullStr	Development of quranic reciter identification system using MFCC and GMM classifier
title_full_unstemmed	Development of quranic reciter identification system using MFCC and GMM classifier
title_sort	development of quranic reciter identification system using mfcc and gmm classifier
publisher	Institute of Advanced Engineering and Science (IAES)
publishDate	2018
url	http://irep.iium.edu.my/64205/ http://irep.iium.edu.my/64205/ http://irep.iium.edu.my/64205/ http://irep.iium.edu.my/64205/1/64205_Development%20of%20Quranic%20Reciter%20Identification%20System_article.pdf http://irep.iium.edu.my/64205/2/64205_Development%20of%20Quranic%20Reciter%20Identification%20System_scopus.pdf
first_indexed	2023-09-18T21:31:04Z
last_indexed	2023-09-18T21:31:04Z
_version_	1777412514281160704

Development of quranic reciter identification system using MFCC and GMM classifier

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