Research on Music Visualization Based on Graphic Images and Mathematical Statistics

Abstract

Music visualization can present music information through the visual way of graphic images, which is helpful to improve the accuracy and effectiveness of music information communication. In view of the shortcomings in the current music visualization field, this paper combines K-means clustering, fusion decision tree and other mathematical statistical methods on the basis of music graphic images to construct a music visualization model based on graphic images and mathematical statistics. First, the application principles of Schlieren imaging and laser Doppler imaging in the visualization of music graphic images are described. Secondly, on the basis of music graphic images, K-means clustering method is used to perform cluster analysis on music visualization information. Finally, through the fusion decision tree method, the classification of music visual information is studied. The actual case analysis and performance test results show the superiority of the music visualization method based on graphic images and mathematical statistics. This method can provide a scientific reference model and basis for the modern music industry to establish new visualization systems using graphics, images and mathematical statistics.