The Application of Artificial Intelligence Technology in Music Education

Abstract

Artificial intelligence (AI) is transforming the field of education by emulating human cognitive processes, boosting adaptability, and improving efficiency across various domains. This integration of AI with music education, which involves a rich tapestry of history, culture, theory, and instrument instruction, facilitates the efficient utilization of musical resources. The growing demand for AI-driven online education is reshaping the landscape of learning and music instruction, particularly in the realm of music education, where the amalgamation of AI technology heralds innovative teaching approaches. This article reviews three significant AI-driven approaches: the "Flipped Classroom", which redefines traditional teaching paradigms by seamlessly combining online and offline learning experiences; the "Music Learning System Based on the RBF Algorithm", employing intricate neural network structures and large-scale music samples for personalized music education; and the "Music Learning System Based on the SCMA Algorithm", which utilizes advanced multiuser detection algorithms for tailored learning experiences. Each method leverages AI techniques, including Convolutional Neural Networks (CNN) and sophisticated algorithms, to enhance content delivery, automate feature extraction, and differentiate individual student needs. These approaches collectively signify a paradigm shift in music education, fostering personalized, interactive, and efficient learning environments for diverse learners. This article proposes future developments that could revolutionize the landscape of AI in music education. The "Singing Teaching System" mentioned in the article creates open-access AI music education software accessible to non-music majors.