用音频和早期播放数据预测歌曲能否登顶流媒体榜单
Prediction of Spotify Chart Success Using Audio and Streaming Features
- 结合音频特征与早期播放数据构建分类模型
- 树模型准确率达97%，宏F1超0.95
- 仅靠音频也能预判爆款，适合音乐产业决策
Spotify的流媒体排行榜为音乐流行度提供了实时视角，影响发现、播放列表推荐甚至收入潜力。理解歌曲在榜单上迅速上升的驱动因素，可指导营销策略、投资决策与创作方向。本研究构建了一个分类流程，基于音乐特征与早期互动数据预测歌曲的榜单成功概率。利用2024年美国每日Top 200榜单及Spotify Web API，建立了包含14,639首独特歌曲的元数据与音频特征数据集。项目分两阶段：第一阶段使用逻辑回归、K近邻、随机森林和XGBoost进行基准测试；第二阶段引入交叉验证、超参数调优与细粒度类别评估以确保稳健性。树模型表现最佳，随机森林与XGBoost的宏F1得分接近0.95，准确率约97%。即使排除流媒体数量与排名历史，仅基于音频特征的模型仍具预测能力。结果证实了音频建模在艺人发掘、播放列表优化与热门歌曲预测中的潜力，可在歌曲达到爆发前提供前瞻性判断。
原文摘要 · Abstract (English)
Spotify's streaming charts offer a real-time lens into music popularity, driving discovery, playlists, and even revenue potential. Understanding what influences a song's rise in ranks on these charts-especially early on-can guide marketing efforts, investment decisions, and even artistic direction. In this project, we developed a classification pipeline to predict a song's chart success based on its musical characteristics and early engagement data. Using all 2024 U.S. Top 200 Spotify Daily Charts and the Spotify Web API, we built a dataset containing both metadata and audio features for 14,639 unique songs. The project was structured in two phases. First, we benchmarked four models: Logistic Regression, K Nearest Neighbors, Random Forest, and XGBoost-using a standard train-test split. In the second phase, we incorporated cross-validation, hyperparameter tuning, and detailed class-level evaluation to ensure robustness. Tree-based models consistently outperformed the rest, with Random Forest and XGBoost achieving macro F1-scores near 0.95 and accuracy around 97%. Even when stream count and rank history were excluded, models trained solely on audio attributes retained predictive power. These findings validate the potential of audio-based modeling in A&R scouting, playlist optimization, and hit forecasting-long before a track reaches critical mass.
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