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...

Full description

Saved in:
Bibliographic Details
Main Authors: Khairuddin, Safiah, Ahmad, Salmiah, Embong, Abd Halim, Nik Hashim, Nik Nur Wahidah, Hassan, Surul Shahbudin
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2019
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
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Universiti Islam Antarabangsa Malaysia
Language: English
English
Description
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