With the development of art education and information technology, it is increasingly necessary to use computer technology and multimedia technology to assist teaching in the teaching activities of music subjects nowadays, so as to cultivate students’ independent inquiry ability and drilling ability. The design of an interactive teaching music intelligence system based on artificial intelligence is studied, and a music learning model based on the RBF algorithm is proposed, which helps to enhance students’ inquiry ability and also plays the leading role of teachers. By teaching each other, students become the main subject of teaching and learning, and it stimulates students’ enthusiasm and learning awareness of music learning.
The study and implementation of music art teaching quality monitoring systems as a major teaching management project in Chinese colleges and universities has been in continuation for a decade. An essential challenge in the management of colleges and universities is that how to assess the teaching abilities of music and art professors fairly and objectively. The system is designed and implemented by separating the front end and back end of browser + server to provide examination services in the way of cloud computing. This system primarily gathers the students’ multidimensional data including sound intensity, pitch, and beat. Then a grey neural network is used to analyze the students’ sound intensity, pitch, and beat. After evaluation process, the scores of the final examination in colleges and universities are uploaded to the backstage server in real-time.
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