Audiovisual Action Aligned Adaptive Feedback System for Interactive Virtual Piano Performance Tutor

With the rapid advancement of computer vision, audio processing, and artificial intelligence technologies, virtual music education systems have increasingly demonstrated significant advantages in piano instruction by overcoming temporal and spatial constraints, enabling personalized guidance, and promoting equitable access to resources. However, most existing virtual piano tutoring systems remain focused primarily on capturing and analyzing audio information, neglecting the crucial role of the performers physical movements in technical execution and musical expression. This leads to feedback that is often limited and unidimensional. Furthermore, the lack of efficient multimodal alignment mechanisms hinders accurate identification of learners overall performance. This study proposes an adaptive feedback system based on audiovisual action alignment, aiming to integrate visual and auditory information through standard performance templates and real-time representation analysis to deliver multidimensional and precise feedback on learners piano playing behaviors. Experimental results show that, compared with traditional video-based instruction, learners using this system exhibited significant improvements in rhythm accuracy and fingering correctness, with all measured indicators reaching statistical significance. The research not only offers a novel technical solution for virtual music education by addressing the limitations of conventional systems in guiding technical actions, but also provides an important theoretical foundation for the design and optimization of intelligent tutoring systems.