5-13 - Detecting Collaboration Profiles in Success-based Music Genre Networks
Gabriel Oliveira, Mariana Santos, Danilo B Seufitelli, Anisio Lacerda, Mirella M Moro
Keywords: MIR tasks, Pattern matching and detection, Domain knowledge, Machine learning/Artificial intelligence for music, Evaluation, datasets, and reproducibility, Novel datasets and use cases, Musical features and properties, Musical style and genre
Abstract: We analyze and identify collaboration profiles in success-based music genre networks. Such networks are built upon data recently collected from both global and regional Spotify weekly charts. Overall, our findings reveal an increase in the number of distinct successful genres from high-potential markets, pointing out that local repertoire is more important than ever on building the global music ecosystem. We also detect collaboration patterns mapped into four different profiles: Solid, Regular, Bridge and Emerging, wherein the two first depict higher average success. These findings indicate great opportunities for the music industry by revealing the driving power of inter-genre collaborations within regional and global markets.