Detection of the Tajweed rules in the Qur’anic recitations

Abstract

Tajweed is the science of reciting the Holy Quran, focusing on the clarity and correctness of recitation. This paper aims to accurately detect the spoken Tajweed rules applied during Quranic recitation, providing a well-structured Tajweed rules database for further analysis, Tajweed learning, and the training of advanced classification models. The main contribution of this work is to identify a high-accuracy approach for Tajweed rules detection and analysis. An improved template matching approach is introduced to enhance detection accuracy by matching the Quranic verse audio file with multiple speech patterns of a specific rule and selecting the best match. The Quranic audio file is segmented into smaller patterns by finding the correlation between the adjacent audio frames. Then, the template matching is applied to these segmented patterns to identify the best-matching ones. The template matching technique relies on a Tajweed database of 487 patterns of the Madd, Noon Sakinah, Tanween, and Meem Sakinah rules. An overall detection accuracy of 97.1% is achieved, and the Tajweed-pattern database is expanded to include the newly detected rules, increasing their total count to 2,583. Furthermore, an application based on the detected rules in this study was developed to enhance the performance of new Tajweed learners.