Remote Music Learning Based on Wireless Sensors Supporting 6G and CPS

This paper presents the Hybrid Dilated Convolutional Neural Network (HDCNN) based remote music learning process, a novel method that uses Wireless Sensors, 6G technology, and Cyber-Physical Systems (CPS). The HDCNN design effectively improves the model's capacity to evaluate high-resolution audio inputs by combining several advanced neural network techniques with dilated convolutions. By adding dilated convolutions, the HDCNN can capture a broader range of musical details and patterns important for effective music teaching and maintaining computational efficiency. Due to the real-time processing requirements of remote music learning, where latency and data throughput are major problems, this capability is significant. This architecture provides a flexible, effective solution for the challenging task of remote music learning in addition to addressing the shortcomings of conventional CNN, in processing complex audio input. A dynamic and adaptable learning platform is supported by the HDCNN's ability to understand difficult musical patterns through variable dilation rates and a multi-scale processing method. Our methodology, which bridges the gap between digital and physical learning locations using cutting-edge technology, marks an important advance in remote music learning. The upcoming sections clearly illustrate the proposed architecture efficiency in remote music learning.