HSP Datasets: Insights on Song Popularity Prediction

Estimating the success of a song before its release is an important music industry task. This work uses audio descriptors to predict the success (popularity) of a song, where typical measures of success are chart measures such as peak position and streaming measures such as listener-count. Currently, a wide range of datasets is used for that purpose, but most of them are not publicly available; likewise, available datasets are restricted either in size, available features, or popularity measures. This substantially impedes the evaluation of the predictive power of a wide range of models. Therefore, we present two novel datasets called HSP-S and HSP-L based on data from AcousticBrainz, Billboard Hot 100, the Million Song Dataset, and last.fm. Both datasets contain audio features, mel-spectrograms as well as streaming listener- and play-counts. The larger HSP-L dataset contains 73,482 songs, whereas the smaller HSP-S dataset contains 7736 songs and additionally features Billboard Hot 100 chart measures. In contrast to the previous publicly available datasets, our datasets contain substantially more songs and richer and more diverse features. We solely utilize data from the public domain, allowing us to evaluate and compare a wide range of models on our datasets. To demonstrate the use of the datasets, we perform regression and classification (popular/unpopular) tasks on both datasets using a wide variety of models to predict song popularity for all provided target measures of success.