Music Genre Trend Prediction Based on Spatial-Temporal Music Influence and Euclidean Similarity

摘要

This paper is concerned with the music genre trends prediction according to the music influence and similarity through data analysis. By a Deepwalk and Cosine Similarity method, a mathematical model is formulated for the music influence, which evaluates the influence of musicians and between musicians. The music similarity model is proposed based on the Principal Component Analysis and Euclidean distance, which summarizes some phenomena according to genres. The genre-independent influence reflects the information about cultural influence. The visual demonstration of influence and similarity are provided. Finally, the trends of music genre are summarized and predicted from the music influence and similarity.