Remote Music Teaching Classroom Based on Machine Learning and 5G Network Station

Abstract

Most recently, the technological impacts are increasing day by day. Artificial intelligence, machine learning, deep learning, and big data are used to acquire enough application feasibility to reduce work pressure for humans. The way information society is being developed in this world will result in some changes in the development of the education system. The impact will result in some changes in the development of the education system. This research focuses on improving remote music teaching following the presence of a 5G network. Data perception handling is considered the central issue in remote communication, which creates some inabilities to achieve better communication between the systems. To avoid this kind of risk management, most schools and colleges have planned to create a separate network working under wireless connections while delivering the course content to a minimum number of students. Another effective integration of this evolution is efficiently building the remote music education system. Here the compatibility should match both the cases, be they students or the teachers. In this case, network speed is considered one of the most significant impacts on students listening to online classes. Therefore, a 5G network might be the best path to take by increasing the network speed and giving a strong impetus. The proposed system utilizes convolutional neural network (CNN) algorithm to train the intelligent system to provide remote music education through wireless 5G networks. The parameters used for analyzing the proposed system with the existing system are compline, uncertainty, unconfirmed, and out-of-place. The proposed algorithm has achieved an overall accuracy of 99.13% than the existing system.

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