Song popularity prediction using machine learning
Forecasting the future popularity of songs holds significant appeal for the music industry. Potential applications encompass evaluating the prospects of a novel song, developing automated songwriting aides, and designing song recommendation systems. There are many factors that influence a song’s pop...
Saved in:
| 主要作者: | |
|---|---|
| 其他作者: | |
| 格式: | Thesis-Master by Coursework |
| 語言: | English |
| 出版: |
Nanyang Technological University
2024
|
| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/173954 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | Forecasting the future popularity of songs holds significant appeal for the music industry. Potential applications encompass evaluating the prospects of a novel song, developing automated songwriting aides, and designing song recommendation systems. There are many factors that influence a song’s popularity, and the problem of predicting the future of a song to be released is even more difficult
to solve. The rapid development of machine learning models provides a feasible solution to this problem. This dissertation uses multiple machine learning models to predict song popularity. Then the dataset is created for research from Spotify. The machine learning model is tested using two methods: machine
learning algorithm and deep learning model. Among the seven machine learning algorithms used in the research, XGBOOST achieved the best results. After using the deep learning model, the model trained through the convolutional neural network achieved higher results than XGBOOST. |
|---|