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Development of Quranic Reciter Identification System using MFCC and GMM Classifier

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 8 No. 1 (2018)

DOI
10.11591/ijece.v8i1.pp372-378
ISSN
2088-8708, 2088-8708
Published
2018-02-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

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.

Collaboration network of the authors

25 authors · 58 co-authorship links · 19 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • International Islamic University Malaysia
  • Syarif Hidayatullah State Islamic University Jakarta
  • Yogyakarta State University
  • University of Kuala Lumpur
  • University of Indonesia
  • Politeknik Negeri Cilacap
  • Lac Hong University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Hasmah MansorInternational Islamic University Malaysia4
Rashidah Funke OlanrewajuInternational Islamic University Malaysia2
Amart SulongInternational Islamic University Malaysia2
Othman Omran KhalifaInternational Islamic University Malaysia2
Hassan DaoUniversity of Kuala Lumpur2
Abdul MutholibInternational Islamic University Malaysia2
Intan Rahmithul Husna YaldiInternational Islamic University Malaysia2
Yasmin Mahira Saiful MunirInternational Islamic University Malaysia2
Tika ArundinaUniversity of Indonesia2
Mohd Azmi OmarInternational Islamic University Malaysia2
Noreha Abdul MalikInternational Islamic University Malaysia1
W. IdrisInternational Islamic University Malaysia1
Siti Noorjannah IbrahimInternational Islamic University Malaysia1
Abd Rahman AhlanAffiliation unknown1
A’ang SubiyaktoSyarif Hidayatullah State Islamic University Jakarta1
Husni Teja SukmanaSyarif Hidayatullah State Islamic University Jakarta1
Mir Suhail AlamInternational Islamic University Malaysia1
Malik Arman MorshidiInternational Islamic University Malaysia1
Fatchul ArifinYogyakarta State University1
Didik HariyantoYogyakarta State University1
Arif Ainur RafiqPoliteknik Negeri Cilacap1
Nguyen Vu QuynhLac Hong University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Development of Quranic Reciter Identification System using MFCC and GMM Classifier</dc:title><dc:creator>Gunawan, Teddy Surya</dc:creator><dc:creator>Muhamat Saleh, Nur Atikah</dc:creator><dc:creator>Kartiwi, Mira</dc:creator><dc:description>&lt;jats:p&gt;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.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2018-02-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v8i1.pp372-378</dc:identifier><dc:identifier>10.11591/ijece.v8i1.pp372-378</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 8 No 1 (2018); 372</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/9815/8181</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>