Classifying Maqams of Qur'anic Recitations Using Deep Learning

The Holy Qur'an is among the most recited and memorized books in the world. For beautification of Qur'anic recitation, almost all reciters around the globe perform their recitations using a specific melody, known as maqam in Arabic. However, it is more difficult for students to learn this art compared to other techniques of Qur'anic recitation such as Tajwid due to limited resources. Technological advancement can be utilized for automatic classification of these melodies which can then be used by students for self-learning. Using state-of-the-art deep learning algorithms, this research focuses on the classification of the eight popular maqamat (plural of maqam). Various audio features including Mel-frequency cepstral coefficients, spectral, energy and chroma features are obtained for model training. Several deep learning architectures including CNN, LSTM, and deep ANN are trained to classify audio samples from one of the eight maqamat. An accuracy of 95.7% on the test set is obtained using a 5-layer deep ANN which was trained using 26 input features. To the best of our knowledge, this is the first ever work that addresses maqam classification of Holy Qur'an recitations. We also introduce the "Maqam-478" dataset that can be used for further improvements on this work.