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Features Identification and Classification of Alphabet ? (ro) in Leaning (Al-Inhiraf) and Repetition (Al-Takrir) Characteristics

机译:特征标识和分类字母表? (RO)倾斜(al-Inhiraf)和重复(Al-Takrir)特征

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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 ? (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 ( Al-Inhiraf) and Repetition (Al-Takrir) characteristics with of 82.1% and 95.8% of accuracy respectively.
机译:穆斯林很重要,以正确地将古兰经与正确的拖拉布牢固地背诵。其中包括使用正确的特征(SiFAAT)和铰接点(Makhraj)。截至目前,研究了有限的研究,重点是根据特征对QURANIC字母进行分类。在这项研究中,焦点对QURANIC字母的特征进行分类,以便开发一种自动自学习系统,以支持QURANIC教学和学习的传统方法。古兰经字母的特征,这是本文的重点是倾斜和重复,两者都包括? (RO)字母表。实施了几种特征提取和分析方法,例如Grentant分析,功率谱密度(PSD)和MEL频率谱系码(MFCC),以最能代表字母表的正确特性的合适特征。一旦确定了特征,使用线性判别分析(LDA)和二次判别分析(QDA)作为分类器。结果表明,所有19个功能的QDA培训均可达到倾斜(Al-Inhiraf)和重复(Al-Takrir)特性的最高百分比精度,分别具有82.1%和95.8%的准确性。

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