A weighted multimodal fusion and deep learning framework for quantitative evaluation of music performance effects

Key Points

• This research aims to quantitatively evaluate the effects of music performance through a novel multimodal fusion and deep learning framework.
• Developed a weighted multimodal fusion framework integrating various input types for analysis.
• Utilized deep learning algorithms to assess and score music performances quantitatively.
• Conducted evaluations with multiple datasets representing different music styles.
• The framework achieved a high accuracy rate in performance evaluation compared to traditional methods.
• Statistical analysis showed a significant improvement in performance scoring (p<0.01).
• Results indicate potential for broader applications in music education and performance assessment.

Abstract