Quranic Reciter Recognition: A Machine Learning Approach

Abstract

Recitation and listening of the Holy Quran with Tajweed is an essential activity as a Muslim and is a part of the faith. In this article, we use a machine learning approach for the Quran Reciter recognition. We use the database of Twelve Qari who recites the last Ten Surah of Quran. The twelve Qari thus represents the 12-class problem. Two approaches are used for audio representation, firstly, the audio is analyzed in the frequency domain, and secondly, the audio is treated as images through Spectrogram. The Mel Frequency Cepstral Coefficients (MFCC) and Pitch are used as the features for model learning in the first case. In the second case of audio as images, Auto-correlograms are used to extract features. In both cases, the features are learned with the classical machine learning which includes the Naïve Bayes, J48, and the Random Forest. These classifiers are selected due to their overall good performance in the state-of-the-art. It is observed that classifiers can efficiently learn the separation between classes, when the audio is represented by the MFCC, and the Pitch features. In such a case, we get 88% recognition accuracy with the Naïve Bayes and the Random Forest showing that Qari can be effectively recognized from the recitation of the Quranic verses.

An End-to-End Transformer-Based Automatic Speech Recognition for Qur’an Reciters

“…In [25], the authors used features such as MFCC and Pitch for learning process to recognize the Qur'an reciters. Several machine learning algorithms such as Random Forest, Naïve Bayes and J48 were used.…”

Identifikasi Irama Tilawah al-Quran dengan Gaya Mujawwad Menggunakan Naive Bayes Classifier

“…Salah satu algoritma populer dan otoritatif dalam machine learning adalah naive bayes classifier (NBC). NBC merupakan algoritma klasifikasi yang sangat efektif (mendapatkan hasil yang tepat) dan efisien (proses penalaran dilakukan dengan memanfaatkan input yang ada dengan cara yang relatif cepat) [7]. Berdasarkan hipotestis peneliti, metode ini mampu mengidentifikasi jenis irama tilawah gaya mujawwad karena NBC memiliki kelebihan dalam menghasilkan tingkat akurasi yang baik [8].…”

“…Recall dapat menjawab pertanyaan "berapa persen irama tilawah yang diprediksi bayyati/hijaz/sikah/jiharkah/shoba/rost/nahawan dibandingkan dengan keseluruhan irama tilawah yang sebenarnya bayyati/hijaz/sikah/jiharkah/shoba/rost/nahawan?". Recall dapat dihitung dengan Persamaan (7).…”

Artificial Intelligence Approach for Target Classification: A State of The Art

“…In front of a lot of circumstances, the algorithm learns which behavior to follow and decision to take creating a model. The machine makes the tasks automate depending on the situation [54][55][56][57][58][59][60][61][62]. There are three main types of Machine Learning [60]- [66] represented in figure 7: In supervised learning, the algorithms are based on already categorized datasets, in order to understand the criteria used for classification and reproducing them [67]- [70].…”