Scalable Computational Techniques for Performance Movement Analysis of Musicians through Image Processing

Abstract

This study may increase performance by offering insider perspectives on implementation. Musical creativity is limited by traditional movement analysis. Two of these drawbacks are slow feedback and poor accuracy in recording minor motions. Traditional performance analysis has drawbacks, including the inability to record minor activities, subjective interpretations, and reduced accuracy. However, it cannot provide exact insights that increase performance and operational efficiency. These issues may be addressed using scalable Image Processing-based Musician Movements (IP-MM). High-resolution cameras and strong image processing algorithms allow this approach to observe and evaluate artists’ movements. IP-MM provides musicians with quick movement style feedback. IP-MM recognized data trends to enhance strategies and performance. This technique greatly improves movement analysis and gives gamers immediate and meaningful feedback. Improving performance demands prioritizing practice. System performance analysis has improved in IP-MM. As a powerful instrument, it lets musicians push their skills. The new technique improves the performance ratio by 97.2%, the practice efficiency ratio by 98.2%, and the movement patterns ratio by 96.32%.