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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Institute of Advanced Engineering and Science (IAES)
2018
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| Subjects: | |
| 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 |
| Summary: | 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. |
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