Deep Learning-Based Assessment and Optimization of Music Performance Ability

The study aims to develop a DL-based evaluation model to achieve quantitative assessment and provide constructive feedback on Music Performance Ability. Designing a deep learning-based assessment model independently extracts and analyzes the key features of music performances (i.e., pitch, rhythm, and emotional expression) in real-time. Performers can receive real-time, precise, and personalized performance feedback through a real-time system and visual presentation. Moreover, the model proposes an individualized training suggestion generation mechanism so that performers will have a target to eliminate skills weaknesses. The results show that the DL-based assessment model significantly enhances musical performers' skill finesse and emotional expression ability. This paper validates the effectiveness of the optimization strategy by comparing the performance ability improvement of the experimental group with the control group. The proposed model brings new tools and methods for music education and performance and provides suggestions for future intelligent music development.