用卷积神经网络结合音频频谱与流媒体数据，预测歌曲流行度。
Predicting Music Track Popularity by Convolutional Neural Networks on Spotify Features and Spectrogram of Audio Waveform
- 基于音频频谱和Spotify特征的CNN模型，捕捉流行规律。
- 在多风格多时段数据上达97%的F1分数，效果优异。
- 适合音乐产业做新歌成功预判，具实用价值。
在数字流媒体环境中，艺术家和行业专家越来越难以预测歌曲的成功。本研究提出一种创新方法，利用卷积神经网络（CNN）和Spotify数据分析来预测歌曲流行度。该方法融合了基于音频波形频谱的声学特征、元数据及用户参与度指标，捕捉影响歌曲流行性的复杂模式与关系。基于涵盖多种流派和人群的大规模数据集，所提出的CNN模型表现出显著预测能力。我们还进行了大量实验，评估模型在不同音乐风格和时间段下的强度与适应性，结果令人鼓舞，达到97%的F1分数。本研究不仅揭示了数字音乐消费动态，也为音乐产业提供了先进的歌曲成功预测工具。
原文摘要 · Abstract (English)
In the digital streaming landscape, it's becoming increasingly challenging for artists and industry experts to predict the success of music tracks. This study introduces a pioneering methodology that uses Convolutional Neural Networks (CNNs) and Spotify data analysis to forecast the popularity of music tracks. Our approach takes advantage of Spotify's wide range of features, including acoustic attributes based on the spectrogram of audio waveform, metadata, and user engagement metrics, to capture the complex patterns and relationships that influence a track's popularity. Using a large dataset covering various genres and demographics, our CNN-based model shows impressive effectiveness in predicting the popularity of music tracks. Additionally, we've conducted extensive experiments to assess the strength and adaptability of our model across different musical styles and time periods, with promising results yielding a 97\% F1 score. Our study not only offers valuable insights into the dynamic landscape of digital music consumption but also provides the music industry with advanced predictive tools for assessing and predicting the success of music tracks.
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