Social entertainment robot based on neural network algorithm in personalized music course simulation

Abstract

This study focuses on the simulation of personalized music courses using social entertainment robots based on neural network algorithms, and introduces the current research status of music processing algorithms based on neural networks. A detailed explanation was provided on the training methods for neural network algorithms, including BP neural network training and music generation algorithms. Research was conducted on activation functions, music output detection, and loss function detection. A virtual music course system framework has been designed to provide students with personalized learning experiences. In a social entertainment robot based on neural network algorithms, a complete control node system was reconstructed, aiming to achieve interactive and entertainment effects of music courses through sensing technology. The control node system includes multiple key components, and the robot is able to perceive and recognize the actions, facial expressions, and sounds of students. Social entertainment robots have intelligent classification and recommendation functions, and this algorithm can intelligently classify and recommend music resources based on students’ music preferences, levels, and needs. By analyzing the personal preferences and learning progress of students, robots can provide customized music course content and learning materials for each student, improving learning effectiveness and personal interests.