Analyses of Musical Success based on Time, Genre and Collaboration - Oliveira, Gabriel P.; Lacerda, Anisio; Moro, Mirella M. (Anais do Concurso de Teses e Dissertações da SBC (CTD-SBC); 2022: Anais do XXXV Concurso de Teses e Dissertações, 2022)
Music is an alive industry with an increasing volume of complex data that can benefit from Computer Science in different ways. Specifically, Music Information Retrieval is a research field aiming to extract meaningful information from musical content. In this work, we analyze musical success from a genre-oriented perspective. Specifically, we model both artist and genre success timelines to detect and predict continuous periods with higher impact. We also build success-based genre collaboration networks to detect collaboration profiles directly related to success. Furthermore, we mine exceptional genre patterns in the networks where the success deviates from the average. Our findings show that studying genre collaboration is a powerful way to assess musical success by describing similar behaviors within collaborative songs. Overall, our work contributes to both the academy and the music industry, as we shed light on the underlying factors of the science behind musical success.