Proper recitation of the Quran, adhering to the rules of Tajweed, is crucial for preventing mistakes during recitation and requires significant effort to master. Traditional methods of teaching these rules are limited by the availability of qualified instructors and time constraints. Automatic evaluation of recitation can address these challenges by providing prompt feedback and supporting independent practice. This study focuses on developing a deep learning model to classify three Tajweed rules - separate stretching (Al Mad), tight noon (Ghunnah), and hide (Ikhfaa) - using the publicly available QDAT dataset, which contains over 1,500 audio recordings. The input data consisted of audio recordings from this dataset, transformed into normalized mel-spectrograms. For classification, the EfficientNet-B0 architecture was used, enhanced with a Squeeze-and-Excitation attention mechanism. The developed model achieved accuracy rates of 95.35%, 99.34%, and 97.01% for the respective rules. An analysis of the learning curves confirmed the model's robustness and absence of overfitting. The proposed approach demonstrates high efficiency and paves the way for developing interactive educational systems for Tajweed study.

翻译：遵循《古兰经》诵读规则（Tajweed）的正确诵读对于防止诵读错误至关重要，且需要付出大量努力才能掌握。传统教学方式受限于合格教师的可及性与时间约束。自动化的诵读评估能够通过提供即时反馈和辅助自主练习来应对这些挑战。本研究致力于开发一种深度学习模型，使用包含1500余条录音的公开QDAT数据集，对三种《古兰经》诵读规则——分离性延长（Al Mad）、鼻音强化（Ghunnah）及隐藏（Ikhfaa）——进行分类。输入数据源自该数据集的音频录音，并转化为归一化的梅尔频谱图。分类任务采用EfficientNet-B0架构，并集成了挤压-激励注意力机制进行增强。所开发的模型对上述三种规则的分类准确率分别达到95.35%、99.34%和97.01%。学习曲线分析证实了模型的鲁棒性且未出现过拟合。所提出的方法展现了高效性，并为开发面向《古兰经》诵读规则学习的交互式教育系统奠定了基础。