AI-Driven Violin Performance Evaluation: A Deep Learning Approach for Automated Assessment and Personalized Feedback in Music Education

Violin performance evaluation normally faces the difficulty of relatively low efficiency and reliance on personal experience in traditional music education. The study focuses on a deep learning automated system, combining an LSTM for temporal analysis for feature extraction of CNN. As well as datasets of the experiment collected 500 participants’ personalized feedback by artificial intelligence. It showed the model of CNN+LSTM that achieved a precision improvement with the error rate decreasing by 16.4%. This study also considers Transformer-based models, such as Music Transformer and MERT, because they can model complex artistic expressions to meet new developments. And the future research will examine Transformer designs to improve the accuracy of feedback and the subtlety of song. Ultimately, the system can create flexible, objective, and adaptable programs in music education training.