Musical Success in the United States and Brazil: Novel Datasets and Temporal Analyses

Authors

  • Gabriel P. Oliveira Universidade Federal de Minas Gerais
  • Gabriel R. G. Barbosa Universidade Federal de Minas Gerais
  • Bruna C. Melo Universidade Federal de Minas Gerais
  • Juliana E. Botelho Universidade Federal de Minas Gerais
  • Mariana O. Silva Universidade Federal de Minas Gerais
  • Danilo B. Seufitelli Universidade Federal de Minas Gerais
  • Mirella M. Moro Universidade Federal de Minas Gerais

DOI:

https://doi.org/10.5753/jidm.2022.2350

Keywords:

hot streaks, hit song science, music information retrieval, open data, time series analysis

Abstract

Music is not only a worldwide essential cultural industry but also one of the most dynamic. The increasing volume of complex music-related data defines new challenges and opportunities for extracting knowledge, benefiting not only different music segments but also the Music Information Retrieval research field. In this article, we assess musical success in the United States and Brazil, two of the biggest music markets in the world. We first introduce MUHSIC and MUHSIC-BR, two novel datasets with enhanced success information that combine chart-related data with acoustic metadata to describe the temporal evolution of musical careers. Then, we use such enriched and curated data to cluster artists according to their success level by considering their high-impact periods (hot streaks). Our results reveal three groups with distinct success behavior over time. Furthermore, Brazil and the US present specific music success patterns regarding artists and genres, reflecting the importance of analyzing regional markets individually.

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Published

2022-08-15

How to Cite

P. Oliveira, G., R. G. Barbosa, G., C. Melo, B., E. Botelho, J., O. Silva, M., B. Seufitelli, D., & M. Moro, M. (2022). Musical Success in the United States and Brazil: Novel Datasets and Temporal Analyses. Journal of Information and Data Management, 13(1). https://doi.org/10.5753/jidm.2022.2350

Issue

Section

Dataset Showcase Workshop 2021 - Extended Papers