Nowadays a large amount of new music emerges every year. How to properly categorize music for quick browsing and retrieval by users and evaluate music popularity based on audio features is an important research topic. In this study, the decision tree model is used to classify music styles on a dataset consisting of audio features of 4802 songs from 2008-2017. Then, the number of music listening in the dataset was used as an indicator to assess the popularity of songs. By comparing the training results of different Machine Learning algorithms on the dataset, Gradient Boosting Regressor is chosen to be used in this case, and the relative importance of different audio features on the popularity of songs was calculated with this model.

Keywords : Audio Features, Machine Learning, Classification