Western Music History Recommendation System Based on Internet-of-Things Data Analysis Technology

Abstract

The quantity of Western music works is growing at an ever-increasing rate, making it difficult for music listeners to identify their favorites quickly. Thus, the music recommendation algorithm targeted music works based on previous user actions, reducing user weariness, and improving overall user experiences. This can minimize the exhaustion experienced by the consumers and increase the overall user experience. In this paper, the Internet of Things-based Western Music Recommendation (IoT-WMR) system can provide music listeners with reliable recommendations. It is used to categorize Western music genres such as traditional music, rock, jazz, and Hip–Hop/Rap. Two distinct activation functions and two different gradient descent techniques are compared and contrasted using the convolutional neural network (CNN). The two classification techniques can be compared depending on the spectrum and feature frequency of the spectrum and musical notes. This paper provides a music classification algorithm that can augment classification approaches in evaluating musical data.