A Machine Learning Model for the Identification of the Holy Quran Reciter Utilizing K-Nearest Neighbor and Artificial Neural Networks

The method of identification of the Holy Quran reciter, which is entered on the various features of the acoustic wave, is referred to as the Holy Quran Reciter Identification. The Muslim communitys Holy Book is the Holy Quran. Listening to or reading the Holy Quran is one of the obligatory activities for Muslims. This research proposes a machine learning model for identifying the Holy Quran reciter using a machine learning language. Here, the presented system comprises the essential phases for a voice recognition system encompassing the processes of classification, extraction of features, preprocessing, and data acquisition. Moreover, the voices of ten known reciters are framed as a dataset in this research. The reciters are leaders of prayers in the Holy masjids of Madinah and Makkah. The analysis of the audio dataset is performed using the mel frequency cepstral coefficients (MFCC). The artificial neural network (ANN) and the k-nearest neighbor (KNN) classifiers are employed for classification. The pitch is utilized as features employed to train the KNN and ANN classifiers. The proposed system is validated using two chapters chosen from the Holy Quran. The results revealed an excellent level of accuracy. With the help of the ANN classifier, the proposed system offered 98.5% accuracy for chapter 7 and 97.2% accuracy for chapter 32. On the other hand, while utilizing KNN, the accuracy for chapter 7 is 97.02% and for chapter 32 is 96.07%. Then, the system’s performance is compared with the utilization of support vector machines (SVM) in recognition of Quranic voice reciter. The comparison results revealed that ANN is a better machine learning algorithm for voice recognition when compared to SVM

Recognition of Correct Pronunciation for Arabic Letters Using Artificial Neural Networks

Automatic speech recognition (ASR) plays an important role in taking technology to the people. There are numerous applications of speech recognition such as direct voice input in aircraft, data entry and speech-to-text processing. The aim of this paper was to develop a voice-learning model for correct Arabic letter pronunciation using machine learning algorithms. The system was designed and implemented through three different phases: signal preprocessing, feature extraction and feature classification. MATLAB platform was used for feature extraction of voice using Mel Frequency Cepstrum Coefficients (MFCC). Matrix of MFCC features was applied to back propagation neural networks for Arabic letter features classification. The overall accuracy obtained from this classification was 65% with an error of 35% for one consonant letter, 87% accuracy and an error of 13% for 10 isolated different letters and 6 vowels each and finally 95% accuracy and an error of 5% for 66 different examples of one letter (vowels, words and sentences) stored in one voice file

Al-Quran Recitation (Tajweed Simulation) “e-Tajweed System”

Al-Quran is the most important book in Muslim‟s life as it gives knowledge in many areas for the use of their daily life. Therefore, it is needed to be read properly so the meaning of the reading is correct. The main purpose of this project is to classify the Tajweed based on letters and signs by defining their shape and location. Images are used as samples to be processed for the use of classification. In an addition, this project was developed parallel to the technology advancement which the interactivity in learning is very important and it influences the understanding of the readers. In order to have a system which has an ability to learn interactively, this project was focused on the implementation of the web-based system. This research has led to the development of e-Tajweed that could monitor individual recitation through internet as well as to the development of practicality of it. As we can see, people nowadays are facing behavioral changes where they want to have a better life especially in the hereafter. They realize it‟s all started from the young education. With the target person are children from primary school, the scope covered for this project is by blending the best approach to be incorporated in order to create rich learning environments that are able to attract children to learn Tajweed and also be able to remember what have been learnt. The methodology used for designing and developing this websites is Rapid Application Development (RAD) which consists of four core phases; planning, analysis, design and development and also implementation. Apart from that, the author also included the results and findings from the survey carried out. Furthermore, the demand for this application in the future will be covered based on the objectives of this project where it enables children to learn Tajweed at home. This will promote independent learning environment where the children could learn Tajweed by themselves or together with their parents or siblings. The active interaction between children and this application will make the learning process more appealing and fun

A Statistical Learning Approach to Evidence the Acoustic Miracles in the Holy Quran Using Audio Features

This paper presents a novel approach for exploring the intrinsic acoustic properties of the Holy Quran, in an attempt to provide yet one more evidence of the miraculous nature of the Quran. The study uses a dataset composed of recitations made by seven prominent reciters and three chapters of the Quran. A novel statistical approach is used to detect the correlation between the recitations of the reciters for three different Chapters (Quranic Surah). The study utilizes the Mel-Frequency Cepstral Coefficients (MFCCs) feature to detect certain common patterns among the recitations. The main measurement indexes used in this study are the correlation and the Euclidian Distance (ED) between the mean of the MFCCs Cepstral Coefficients, and deltadelta MFCCs. The study reveals a strong correlation and short distance between all recitations for one verse at a time, and relatively high correlation and short distance for two or more verses. Furthermore, the study lays down a foundation to detect and formulate acoustic clusters for sequential verses in the Holy Quran

DESIGN OF I-SLA (ISLAMIC LEARNING APPLICATION) AS TAJWEED LEARNING MEDIA BY USING THE SPEECH RECOGNITION TECHNOLOGY

Indonesia is a country with the majority of the population converting to Islam, which is more than 87% of the total population of Indonesia. As Muslims who adhere to the teachings of Islam, the teachings that must be understood are tajweed lessons. Tajweed science is the science that studies how to read the Qur'an properly and correctly. Adherents of Islam in Indonesia are still many who do not understand and cannot read the Qur'an properly and correctly. Research noted that there are still about 65% of Indonesian Muslims still blind to the writings of the Qur'an. The importance of learning tajweed science is that it can read precisely, if there are errors in reading the Quran can change its true meaning. Tajweed lessons are commonly obtained through non-formal educational institutions that focus on learning Islam. The current pandemic period causes all learning activities to be limited and difficult, including learning al quran education.  Online learning applications today are still rare that develop Quran Education including tajweed science, so people who want to learn the science have not found the right tools. We are planning an application called I-SLA (Islamic learning application). I-SLA is a tajweed learning application that utilizes speech recognition to correct the pronunciation of the user's Quran and provide justification if in pronunciation there is still something wrong, this technology has the ability to exchange information using acoustic signals. In addition, there is a consulting feature of tajweed experts if they feel they want to deepen tajweed knowledge. The design of the application in this study was carried out in a direct manner. The mechanism of this research is made by conducting a literature study for the process of making software needs specifications, followed by the creation of software design with UI / UX, followed by the creation of applications and closed with testing. This process is carried out continuously in accordance with the planning period. The result of this study is the application of I-SLA (Islamic Learning Application) with the aim of users of children, adolescents, and adults who want to deepen tajweed science to improve its pronunciation

Makhraj Recognition of Hijaiyah Letter for Children Based on Mel-Frequency Cepstrum Coefficients (MFCC) and Support Vector Machines (SVM) Method

Makhraj is the most important thing for Muslim to recite the Holy Quran properly besides of Tajweed. This paper describe the Makhraj recognition of Hijaiyah Letter for children education. To make the Makhraj recognition, the feature extraction is used Mel-Frequency Cepstrum Coefficients (MFCC) method and to classify the Hijaiyah letter use Support Vector Machines (SVM) method based on Python 2.7. The waveform analysis of each Hijaiyah Makhraj pronunciation shows the differences of each letter. The database of Hijaiyah Makhraj pronunciation using 12 feature extraction can be classified by SVM process

Optimization of Text Mining Detection of Tajweed Reading Laws Using the Yolov8 Method on the Qur'an

The science of tajweed is a science that studies how to read the letters or readings in the Qur'an beautifully or well by the legal rules regulated therein. However, many people still do not pay attention to the legal rules of tajweed when reading the Qur'an, so it is not uncommon for them to make mistakes in pronunciation. From the legal rules of Tajweed reading, the slightest difference will change the meaning and intended meaning of the reading. So, paying attention to every rule of the law of reading Tajweed is very important. Therefore, considering the current technological advances, we plan a tajweed detection design using the YOLO algorithm optimized for the Qur'an. This study aims to determine and analyze the detection of text mining on tajweed reading. The method used in this study is the YOLO Algorithm method. This research uses 210 images of the Mushaf Al-Qur'an dataset, tested twice using Augmentation and Non-Augmentation to get optimal research results. The dataset underwent a training process of 138 images, or about 66%, and a validation process of 48 images, about 28%, and 24 images, or 11% of the total sample. Of the two tests using augmentation with no augmentation, augmentation testing produces the highest precision value with a value of 0.985 or 98.5% and the highest mAP50 with a value of 0995 or 99.5% for the Lafdzul Jalalah class group, with a total accuracy value of 92.94%. For testing without augmentation, the results show that the highest mAP50 value is the Lafdzul Jalalah class, with a value of 0.974 or 97.40% and an accuracy value of 91.37%. Based on optimization and comparison carried out for the accuracy value of research with augmentation of 92.94% and research conducted without augmentation is 91.37%. So, the study's results obtained an increased value of 1.57% by performing greyscale augmentation

Quranic letter pronunciation analysis based on spectrogram technique: case study on Qalqalah letters

Recitation of the Holy Quran with Tajweed is an essential activity as a Muslim. Reciting Quran correctly indicates the correct meaning of the words of Allah has been received from this significant resource among Muslims. That is why Muslims stress on the Quranic Education since in the early age. It is important to pronounce the letter correctly based on its characteristics as well as the articulation point of each letter. In this paper, the characteristic based on Qalqalah letters is considered to be analyzed. The audio signal from a person who is very good at Quranic recitation was taken and analyzed. We implement spectral analysis to find the features of Qalqalah letters and extract the correlation between the first formant frequency and the pharyngeal space of the signal. Spectrogram was successfully implemented and proved this relation, and it described the mechanism of Qalqalah correctly, which is unique as compared to other Quranic letters

Review of utilizing the speech identification system for spoken queries in the Qur'an

The speech recognition system will produce a transcription text from the sound being tested — some speech recognition systems at the Al-Qur'an show quite good accuracy. There is a big possibility to use the speech recognition system to be an input to other systems. The use of sound as input to the system to do searches in data in a text representation is called spoken query. This paper tries to conduct a discussion on the use of speech recognition systems to become spoken queries in the Qur'an data. As a result, the speech recognition system can be used as input to the Qur'anic verse retrieval system and the tajwid checking system