用流媒体和音频特征预测歌曲能否进公告牌百强榜。
Beyond the Hook: Predicting Billboard Hot 100 Chart Inclusion with Machine Learning from Streaming, Audio Signals, and Perceptual Features
- 结合流媒体数据、音频信号与听觉感知特征建模。
- 逻辑回归准确率达90.0%，对上榜歌曲召回率超98%。
- 适合音乐产业研究者与流媒体平台算法团队参考。
数字流媒体平台的兴起彻底改变了音乐产业格局，其带来的结构化数据为研究流行度动态与主流成功机制开辟了新路径。本文探究了哪些因素最能预测一首歌曲是否进入公告牌百强榜（Billboard Hot 100），包括流媒体热度、可测量的音频信号属性以及人类听感的概率指标。分析表明，流行度是决定性因素，其次为乐器性、情绪值、时长和语音含量。逻辑回归模型达到90.0%准确率，对上榜歌曲召回率达0.986，非上榜歌曲召回率为0.813，F1分数约0.90。随机森林将准确率提升至90.4%，对非上榜歌曲保持0.990高精确率，上榜歌曲召回率达0.992，F1最高达0.91。梯度提升（XGBoost）准确率为90.3%，在提升非上榜歌曲召回率（0.837）的同时维持上榜歌曲高召回率（0.969），整体表现均衡，F1分数与其它模型相当。
原文摘要 · Abstract (English)
The advent of digital streaming platforms have recently revolutionized the landscape of music industry, with the ensuing digitalization providing structured data collections that open new research avenues for investigating popularity dynamics and mainstream success. The present work explored which determinants hold the strongest predictive influence for a track's inclusion in the Billboard Hot 100 charts, including streaming popularity, measurable audio signal attributes, and probabilistic indicators of human listening. The analysis revealed that popularity was by far the most decisive predictor of Billboard Hot 100 inclusion, with considerable contribution from instrumentalness, valence, duration and speechiness. Logistic Regression achieved 90.0% accuracy, with very high recall for charting singles (0.986) but lower recall for non-charting ones (0.813), yielding balanced F1-scores around 0.90. Random Forest slightly improved performance to 90.4% accuracy, maintaining near-perfect precision for non-charting singles (0.990) and high recall for charting ones (0.992), with F1-scores up to 0.91. Gradient Boosting (XGBoost) reached 90.3% accuracy, delivering a more balanced trade-off by improving recall for non-charting singles (0.837) while sustaining high recall for charting ones (0.969), resulting in F1-scores comparable to the other models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。