A Comprehensive Approach to Song Popularity Forecasting

Assessing the potential success of a song based on its acoustic characteristics is a crucial task within the music industry. Traditionally, this has been approached by leveraging audio features independently. In this study, we take a novel approach by jointly exploiting audio features, framing as a regression on prediction task. Our methodology employs a wide architecture, enabling a comprehensive analysis. Additionally, we enhance feature sets by incorporating information about track release years. Evaluation on the Million Song Dataset, using Billboard Hot 100 inclusion as a metric for song success, reveals that our approach surpasses baseline methods and those utilizing low- or high-level features in isolation. Notably, the inclusion of release year alongside mood and vocal features further enhances predictive accuracy, highlighting the efficacy of our comprehensive model in gauging song popularity.