Analysis of two adjacent articulation Quranic letters based on MFCC and DTW

—Reciting al-Quran in the correct way is an obligatory duty for Muslims, and therefore learning al-Quran is a continuous education until the correct recitation is achieved. It is important to learn Tajweed rules to master the recitation of Quranic verses. Moreover, mastering the pronunciation of Arabic sounds is the first and key step to achieve accurate recitation of al-Quran. The rules were guided by the Islamic Scholars in fields related to al-Quran from their knowledge and experiences. Very limited researches were found in the perspective of sciences and engineering. In this paper two Quranic letters (ذ and ز) that are articulated from adjacent points of articulation were analyzed using Mel- frequency coefficient analysis. MFCCs matrices were calculated then compared using the dynamic time warping DTW technique to calculate the similarity matrices and find the similarity distance. Results show that letters from the same point of articulation have less similarity distance compared to the letters from different point of articulation

Lips tracking identification of a correct pronunciation of Quranic alphabets for tajweed teaching and learning

Mastering the recitation of the Holy Quran is an obligation among Muslims. It is an important task to fulfill other Ibadat like prayer, pilgrimage, and zikr. However, the traditional way of teaching Quran recitation is a hard task due to the extensive training time and effort required from both teacher and learner. In fact, learning the correct pronunciation of the Quranic letters or alphabets is the first step in mastering Tajweed (Rules and Guidance) in Quranic recitation. The pronunciation of Arabic alphabets is based on its points of articulation and the characteristics of a particular alphabet. In this paper, we implement a lip identification technique from video signal acquired from experts to extract the movement data of the lips while pronouncing the correct Quranic alphabets. The extracted lip movement data from experts helps in categorizing the alphabets into 5 groups and in deciding the final shape of the lips. Later, the technique was tested among a public reciter and then compared for similarity verification between the novice and the professional reciter. The system is able to extract the lip movement of the random user and draw the displacement graph and compare with the pronunciation of the expert. The error will be shown if the user has mistakenly pronounced the alphabet and suggests ways for improvement. More subjects with different backgrounds will be tested in the very near future with feedback instructions. Machine learning techniques will be implemented at a later stage for the real time learning application. Menguasai bacaan Al-Quran adalah satu kewajipan di kalangan umat Islam. Ia adalah satu tugas yang penting untuk memenuhi Ibadat lain seperti solat, haji, dan zikir. Walau bagaimanapun, cara tradisional pengajaran bacaan Al-Quran adalah satu tugas yang sukar kerana memerlukan masa latihan dan usaha yang banyak daripada guru dan pelajar. Malah, mempelajari sebutan yang betul bagi huruf Al-Quran adalah langkah pertama dalam menguasai Tajweed (Peraturan dan Panduan) pada bacaan Al-Quran. Sebutan huruf Arab adalah berdasarkan cara penyebutan tiap-tiap huruf dan ciri-ciri huruf tertentu. Dalam kertas ini, kami membina teknik pengenalan bibir dari isyarat video yang diperoleh daripada bacaan Al Quran oleh pakar-pakar untuk mengekstrak data pergerakan bibir ketika menyebut huruf Al-Quran yang betul. Data pergerakan bibir yang diekstrak daripada pembacaan oleh pakar membantu dalam mengkategorikan huruf kepada 5 kumpulan dan dalam menentukan bentuk akhir bibir. Kemudian, teknik ini diuji dengan pembaca awam dan kemudian bacaan mereka dibandingkan untuk pengesahan persamaan bacaan antara pembaca awam dan pembaca Al-Quran profesional. Sistem ini berjaya mengambil pergerakan bibir pengguna rawak dan melukis graf perbezaan sebutan mereka apabila dibandingkan dengan sebutan pakar. Jika pengguna telah tersilap menyebut sesuatu huruf, kesilapan akan ditunjukkan dan cara untuk penambahbaikan dicadangkan. Lebih ramai pengguna yang mempunyai latar belakang yang berbeza akan diuji dalam masa terdekat dan arahan maklum balas akan diberi. Teknik pembelajaran mesin akan dilaksanakan di peringkat seterusnya bagi penggunaan pembelajaran masa nyata

Characteristics with opposite of quranic letters mispronunciation detection: a classifier-based approach

Reading Quran for non-Arab is a challenge due to different mother tongues. learning Quran face-to-face is considered time-consuming. The correct pronunciation of Makhraj and Sifaat are the two things that are considered difficult. In this paper, Sifaat evaluation system was developed, focusing on Sifaat with opposites for teaching the pronunciation of the Quranic letters. A classifier-based approach has been designed for evaluating the Sifaat with opposites, using machine learning technique; the k-nearest neighbour (KNN), the ensemble random undersampling (RUSBoosted), and the support vector machine (SVM). Five separated classifiers were designed to classify the Quranic letters according to group of Sifaat with opposites, where letters that are classified to the wrong groups are considered mispronounced. The paper started with identifying the acoustic features to represent each group of Sifaat. Then, the classification method was identified to be used with each group of Sifaat, where best models were selected relying on various metrics; accuracy, recall, precision, and F-score. Cross-validation scheme was then used to protect against overfitting and estimate an unbiased generalization performance. Various acoustic features and classification models were investigated, however, only the outperformed models are reported in this paper. The results showed a good performance for the five classification models

Analysis of Formant Frequencies of the Correct Pronunciation of Quranic Alphabets Between Kids and Adults

It is an obligation for a Muslim to become skilled and proficient in reciting Al-Quran considering that Al-Quran is the fundamental source of revelation from Allah SWT. In Al-Quran, there are 28 alphabets where each of them has their own unique sound. The Quranic alphabets produce sound that are characterized from their point of articulation (Makhraj) and their characteristics (Sifaat). Knowing the correct way of pronunciation through engineering perspective may help Muslim in learning Al-Quran, in the sense that the signal of the experts can be used in Quranic teaching and learning as a reference model. Since both adults and children possess different vocal tract, therefore there will be different outcomes of the pronunciation between both experts. The features identification of the pronunciation of both experts is needed to represent the actual and correct pronunciation that will be used as a reference for Quranic teaching and learning at later. In this paper, the focus was on the identification and analysis of the correct pronunciation of the Quranic alphabets on the data obtained from adults and children experts. The first and second formant frequencies (F1 and F2) were used as the features where they were used to represent the pronunciation of each alphabet for both adults and children category. The speech analysis software PRAAT was used to accomplish the pre-processing of the data using Spectral Subtraction technique and also used to measure the F1 and F2 values. Linear Discriminant Analysis (LDA) was used for classification of the signals and results shows that some of the alphabets can be identified uniquely using F1 and F2 features of the two categories

The classification of actual pronunciation of Quranic alphabets for non-Arab speaker

Each Quranic alphabet has its unique sounds produced by uttering the Arabic language at their point of articulation (Makharij) and their characteristics (Sifaat), which is called Tajweed. Much research has been done related to the Tajweed, but there are limited sources associated with the analysis of Quranic pronunciation of non-Arab speakers from signal processing perspective with respect to both Makhraj and Sifaat. Therefore, this research focus on the identification and classification of the actual pronunciation of the Quranic alphabets on the audio signal obtained from non-Arab speakers. In this research, the features were best identified by the combinations of four formant frequencies (f1, f2, f3, f4), and three power spectral density (psd1, psd2, psd3) extracted from the sukoon pronunciation of the alphabets’ audio data. All features were combined and classified using both Linear and Quadratic Discriminant Analysis (LDA and QDA) to represent the actual pronunciation of each alphabet for both Arab and Non-Arab speaker categories. The findings indicate that there are 25 alphabets were correctly classified more than 83% threshold value thus indicates the correct pronunciation, where the other three alphabets which are أ ضْ, أ غْ and أ فْ are falling under misclassified vector. Later, the Graphical User Interface (GUI) was developed using the training data of 25 alphabets to evaluate the percentage of accuracy (%) with the new audio dataset. The results show that the developed GUI has managed to record, analyze, and evaluate the pronunciation interactively

Mechanical design and simulation of wwo-Wheeled wheelchair using solidworks

This article is presented a new design of two-wheeled wheelchair that can balance on two wheels to make it suitable in the narrow areas, especially in the domestic environments; it has the ability to extend the height of the chair to help the user to act independently in the life for example, in the library to pick and put books on the shelves. The 3D model has been built up using SolidWorks Software. Nowadays, SolidWorks environment is considered as a powerful tool that is helping designer to design products and attain its performance before physical prototype stage. SolidWorks simulation model has been employed to test the frame of the wheelchair under the weight of the human body and the upper part of the wheelchair. The static analysis has been done on the frame using steel and aluminium; however the aluminium material has been selected due to its light weigh

Integrated design, modelling and analysis of two-wheeled wheelchair for disabled

Two-wheeled wheelchairs are considered as high nonlinear and complex system. This complex system as well as the need for this system to help disabled people has motivated researchers around the world to do their work in this area. But the recent designs have some limitations. This paper will come with new design to come over these limitations. A new design of two-wheeled wheelchair has been proposed with physical and mathematical models. The system was tested by Linear-quadratic regulator LQR and the result showed that the linear model is working well

Integrated design, modeling and analysis of a two-wheeled wheelchair for disabled

Automatic adjustment rear view mirror system is designed to constitute for the weak points of the manual adjustment rear view mirror. This system determines the optimal position angle of the rear view mirror based on height of the driver, categorised as short, medium and tall. To achieve this target, the driver’s face coordinates are measured by means of an image processing technique. These coordinates are input to a mirror actuation system which rotates the mirror to the target position. This system is very easy, convenient and safe to use, and it is possible to adjust mirrors safely while driving since all processes are performed automatically

Centre of Gravity (C.O.G)-based analysis on the dynamics of the extendable Double-Link Two-Wheeled Mobile Robot

This paper discusses about the analysis on the centre of gravity (C.O.G) in affecting the input reference of the motion control of the extendable double-link of two-wheeled mobile robot. The proposed system mimics double inverted pendulum, where the angular position of the first link (Link1) is to be varied depends on the value of the angular position of the second link (Link2) and the elongation of the extendable-link (Link3) that is attached to Link2 with different payload. The two-wheeled mobile robot together with the extendable link on Link2 makes that system become more flexible but yet, the system has become more unstable. The inclination of extendable link at any interest angle will affect the C.O.G of the system especially when the payload is having a significant weight. This two-wheeled mobile robot can be balanced on the condition that the system’s center of gravity must be located on the centre of the wheels. Therefore the input reference of Link1 will be determined from the C.O.G analysis of the system with the payload. Preliminary results show that the angular position of Link1 can be set at suitable degree based on C.O.G analysis that is used for motion control

Vocal tract shape estimation and analysis of the quranic letters articulated from the deep throat

Recitation of the Quranic verses correctly is an important deed for Muslims. To attain an accurate recitation of Quran, it is important to master the pronunciations technique of the Arabic letters. The traditional way of teaching and learning al-Quran is time consuming and requires a teacher who are expert in Quranic recitation to guide on the correct pronunciation of Makhraj and proper Tajweed, which mostly is done face-to-face. It is significant to provide an interactive platform to assist the teachers as well as expediting the students. This is proposed through the vocal tract shape estimation based on linear predictive analysis (LPC) of speech that can provide a visualized feedback to students during the practice session. This paper conducts the linear prediction analysis to estimate the vocal tract shape on two Arabic letters that are articulated from the deepest part of the throat. The results show that the vocal tract shape can be estimated using linear prediction analysis for these two letters and the shapes are similar to the 2D validated model that is used in the Quranic teaching