Features identification and classification of alphabet (ro) in leaning (Al-Inhiraf) and repetition (Al-Takrir) characteristics
—It is important for Muslim to recite the Quran properly with the correct Tajweed. which includes the use of correct characteristics (sifaat) and point of articulations (makhraj). To this date, there are limited researches done focusing on classifying the Quranic letters according to the charac...
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Main Authors: | , , , , |
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Format: | Conference or Workshop Item |
Language: | English English |
Published: |
Institute of Electrical and Electronics Engineers Inc.
2019
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Subjects: | |
Online Access: | http://irep.iium.edu.my/79729/1/79729_Features%20Identification%20and%20Classification%20_conf.%20article.pdf http://irep.iium.edu.my/79729/2/79729_Features%20Identification%20and%20Classification%20_scopus.pdf http://irep.iium.edu.my/79729/ https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8825067 |
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Institution: | Universiti Islam Antarabangsa Malaysia |
Language: | English English |
Summary: | —It is important for Muslim to recite the Quran
properly with the correct Tajweed. which includes the use of
correct characteristics (sifaat) and point of articulations
(makhraj). To this date, there are limited researches done
focusing on classifying the Quranic letters according to the
characteristics. In this study, the focus is given to the
classification of the characteristics of the Quranic letters for the
purpose of developing an automated self-learning system for
supporting the conventional method of Quranic teaching and
learning. The characteristics of Quranic letters, which are the
focus in this paper are Leaning and Repeating, where both
consists of ر) ro) alphabet. Several methods of feature
extractions and analysis were implemented such as Formant
Analysis, Power Spectral Density (PSD), and Mel Frequency
Cepstral Coefficient (MFCC) to come out with the suitable
features that best represent the correct characteristics of the
alphabet. Once the features had been identified, Linear
Discriminant Analysis (LDA) and Quadratic Discriminant
Analysis (QDA) were used as the classifier. The results show that
QDA with all 19 features trained achieved the highest
percentage accuracy for both Leaning (اإلنحراف – Al-Inhiraf) and
ّكرير) Repetition
الت– Al-Takrir) characteristics with of 82.1% and
95.8% of accuracy respectively |
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