MusicARLtrans Net: a multimodal agent interactive music education system driven via reinforcement learning

This approach provides a personalized and real-time feedback interactive learning experience by effectively combining auditory, visual, and textual information. The system collects and annotates multimodal data related to music education, trains and integrates various modules, and ultimately delivers an efficient and intelligent music education agent. Experimental results demonstrate that MusicARLtrans Net significantly outperforms traditional methods, achieving an accuracy of <b>96.77%</b> on the LibriSpeech dataset and <b>97.55%</b> on the MS COCO dataset, with marked improvements in recall, F1 score, and AUC metrics. These results highlight the system's superiority in speech recognition accuracy, multimodal data understanding, and teaching strategy optimization, which together lead to enhanced learning outcomes and user satisfaction. The findings hold substantial academic and practical significance, demonstrating the potential of advanced AI-driven systems in revolutionizing music education.