In the music market, superstars significantly dominate the market share, while predicting the top hit songs is notoriously difficult. The music intelligence technology, retrieving and utilizing granular acoustic features of songs, provides opportunities to improve the prediction of top hit songs. Using data on 6209 unique songs that appeared in the weekly Billboard Hot 100 charts from 1998 to 2016, especially acoustic features provided by Spotify, we investigate empirically how the top-10-hit-songs likelihood prediction is improved by acoustic features. We find that some acoustic features (e.g., danceability, happiness, and some metrics of timbre and pitch) significantly improve the model's ability to predict the top-10-hit-songs probability. These results suggest that the granular data, provided by the music intelligence technology, carries a substantial predictive value in the era of online music streaming.

在音乐市场上，超级巨星在市场份额中占据着主导地位，而要预测最流行的歌曲却非常困难。音乐情报技术可以检索和利用歌曲的细颗粒声学特征，从而提供了改善对热门歌曲的预测的机会。使用1998年至2016年每周Billboard Hot 100榜单中出现的6209首独特歌曲的数据，尤其是Spotify提供的声学功能，我们根据经验研究声学特征如何改善热门10首热门歌曲的可能性。我们发现某些声学特征（例如，可跳舞性，幸福感以及某些音色和音高指标）显着提高了模型预测前十首热门歌曲概率的能力。这些结果表明，由音乐智能技术提供的粒状数据在在线音乐流媒体时代具有重大的预测价值。