Analyses of Musical Success based on Time, Genre and Collaboration

  • Gabriel P. Oliveira UFMG
  • Anisio Lacerda UFMG
  • Mirella M. Moro UFMG

Resumo


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.

Palavras-chave: Hit Song Science, Music Information Retrieval, Musical Genres, Social Networks, Data Science, Data Mining

Referências

Abel, F. et al. (2010). Analyzing the blogosphere for predicting the success of music and movie products. In ASONAM, pages 276–280, Odense, Denmark.

Askin, N. and Mauskapf, M. (2017). What makes popular culture popular? product features and optimal differentiation in music. Amer. Sociolog. Rev., 82(5):910–944.

Bryan, N. J. and Wang, G. (2011). Musical influence network analysis and rank of samplebased music. In ISMIR, pages 329–334, Miami, USA.

Cosimato, A. et al. (2019). The conundrum of success in music: Playing it or talking about it? IEEE Access, 7:123289–123298.

Dhanaraj, R. and Logan, B. (2005). Automatic prediction of hit songs. In Procs Int’l Conf. on Music Information Retrieval, pages 488–491, London, UK. ISMIR.

Garimella, K. and West, R. (2019). Hot streaks on social media. In International Conference on Web and Social Media, pages 170–180. AAAI Press.

Janosov, M. et al. (2020). Success and luck in creative careers. EPJ Data Sci., 9(1):9.

Keogh, E. J. and Pazzani, M. J. (2000). Scaling up dynamic time warping for datamining applications. In Int’l Conf. Knowledge Discovery and Data Mining, pages 285–289.

Klösgen, W. and Zytkow, J. M. (2002). Handbook of data mining and knowledge discovery. Oxford University Press, Inc.

Oliveira, G. P., Barbosa, G. R. G., Melo, B. C., Silva, M. O., Seufitelli, D. B., Lacerda, A., and Moro, M. M. (2021). MUHSIC: Music-oriented Hot Streak Information Collection. https://doi.org/10.5281/zenodo.4779003.

Oliveira, G. P., Silva, M. O., Seufitelli, D. B., Lacerda, A., and Moro, M. M. (2020). MGD: Music Genre Dataset. https://doi.org/10.5281/zenodo.4778563.

Ren, J. and Kauffman, R. J. (2017). Understanding music track popularity in a social network. In Euro. Conf. Information Systems, pages 374–388, Atlanta, USA. AIS.

Shin, S. and Park, J. (2018). On-chart success dynamics of popular songs. Advances in Complex Systems, 21(3-4):1850008.

Silva, M. O. et al. (2019). Collaboration Profiles and Their Impact on Musical Success. In Procs. of ACM/SIGAPP SAC, pages 2070–2077, Limassol, Cyprus.

Silva, M. O. and Moro, M. M. (2019). Causality analysis between collaboration profiles and musical success. In WebMedia, pages 369–376. ACM.

Sinatra, R., Wang, D., Deville, P., Song, C., and Barabási, A.-L. (2016). Quantifying the evolution of individual scientific impact. Science, 354(6312).

Suh, M. M. et al. (2021). AI as social glue: Uncovering the roles of deep generative AI during social music composition. In CHI, pages 582:1–582:11. ACM.

Zaki, M. J. and Meira Jr., W. (2014). Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press.
Publicado
31/07/2022
OLIVEIRA, Gabriel P.; LACERDA, Anisio; MORO, Mirella M.. Analyses of Musical Success based on Time, Genre and Collaboration. In: CONCURSO DE TESES E DISSERTAÇÕES (CTD), 35. , 2022, Niterói. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 81-90. ISSN 2763-8820. DOI: https://doi.org/10.5753/ctd.2022.223090.