Live Music Industry and Common Music Trend Prediction in Machine Learning Algorithms

As live music became popular, many music platforms emerged, and the music industry experienced a dramatic development. To further develop the music industry, music trend prediction is necessary for music companies and composers. The paper aims to provide a detailed description of the current situation in the music industry and showcase the procedures and some common methods used by other researchers to predict music trends. Those methods are all based on training models, including big data algorithms making mathematical models, feature extraction from the songs, utilizing Self-Embedding Attention Layer (SEAL) framework and Graph Neural Networks (GNNs), and using the influence data set and Integrative Collective Music (ICM). These methods could predict the music trends on certain platforms or data sets. Meanwhile, they also have some drawbacks, such as a lack of data access, especially data with high quality, and the static characteristic since the trend of pop music always changes. Concerning the models created by those authors, some possible future perspectives, like establishing a public data set and further support for the music industry, are proposed, attempting to summarize the field of music trend prediction in current society.