Design of Online Music Education System Based on Artificial Intelligence and Multiuser Detection Algorithm

Area of Science:

• Educational Technology
• Computer Science
• Music Education

Background:

• Online music education has surged, accelerated by the COVID-19 pandemic.
• There is a critical need for advanced systems to improve the quality of online music teaching.
• Multiuser detection algorithms and artificial intelligence offer potential solutions for educational challenges.

Purpose of the Study:

• To design and analyze an online music education system.
• To leverage multiuser detection and artificial intelligence for music distance teaching.
• To improve the overall effectiveness of online music education for teachers, students, and administrators.

Main Methods:

• Utilized the SCMA (Sparse Code Multiple Access) system multiuser detection algorithm.
• Applied artificial intelligence techniques for system analysis and design.
• Developed system modules for information management, assignments, and online courses.

Main Results:

• The designed system effectively supports music distance teaching.
• Modules include basic information management, student music assignments, and online courses.
• The system serves as a model for online music education system design.

Conclusions:

• The online music education system enhances music learning efficiency.
• The integration of multiuser detection and AI yields significant benefits for music learners.
• This approach provides a robust framework for future online music education development.