From Compact Discs to Streaming: A Comparison of Eras within the Brazilian Market
Visualizações: 460DOI:
https://doi.org/10.33871/23179937.2022.10.1.2Keywords:
Brazilian music market, musical success, music information retrieval, time series analysis, hot streaksAbstract
The music industry has undergone many changes in the last few decades, notably since vinyl, cassettes and compact discs faded away as streaming platforms took the world by storm. This Digital evolution has made huge volumes of data about music consumption available. Based on such data, we perform cross-era comparisons between Physical and Digital media within the music market in Brazil. First, we build artists' success time series to detect and characterize hot streak periods, defined as high-impact bursts that occur in sequence, in both eras. Then, we identify groups of artists with distinct success levels by applying a cluster analysis based on hot streaks' features. We find the same clusters for both Physical and Digital eras: Spike Hit Artists, Big Hit Artists, and Top Hit Artists. Our results reveal significant changes in the music industry dynamics over the years by identifying the core of each era.Downloads
References
ARAUJO, Carlos Soares; CRISTO, Marco; GIUSTI, Rafael. Predicting music popularity on streaming platforms. In: Proceedings of the 17th Brazilian Symposium on Computer Music. Porto Alegre: SBC, 2019. p. 141-148. DOI: https://doi.org/10.5753/sbcm.2019.10436
ARAÚJO LIMA, Raul de et al. Brazilian lyrics-based music genre classification using a BLSTM network. In: International Conference on Artificial Intelligence and Soft Computing. Cham: Springer, 2020. p. 525-534. DOI: https://doi.org/10.1007/978-3-030-61401-0_49
BARBOSA, Gabriel R. G.; MELO, Bruna C.; OLIVEIRA, Gabriel P.; SILVA, Mariana O.; SEUFITELLI, Danilo B.; MORO, Mirella M. Hot Streaks in the Brazilian Music Market: A Comparison Between Physical and Digital Eras. In: Proceedings of the 18th Brazilian Symposium on Computer Music. Porto Alegre: SBC, 2021, p. 155-162. DOI: https://doi.org/10.5753/sbcm.2021.19440
BHOLOWALIA, Purnima; KUMAR, Arvind. EBK-means: A clustering technique based on elbow method and k-means in WSN. International Journal of Computer Applications, v. 105, n. 9, 2014.
BORGES, R.; QUEIROZ, Marcelo. A probabilistic model for recommending music based on acoustic features and social data. In: 16th Brazilian Symposium on Computer Music, 16, São Paulo, Brazil. 2017. p. 7-12.
CORRÊA, Débora C.; RODRIGUES, Francisco Ap. A survey on symbolic data-based music genre classification. Expert Systems with Applications, v. 60, p. 190-210, 2016. DOI: https://doi.org/10.1016/j.eswa.2016.04.008
DE MARCHI, Leonardo; LADEIRA, João Martins. Digitization of music and audio-visual industries in Brazil: new actors and the challenges to cultural diversity. Les Cahiers d"™Outre-Mer. Revue de géographie de Bordeaux, v. 71, n. 277, p. 67-86, 2018. DOI: https://doi.org/10.4000/com.8716
DE MELO, Gabriel Borges Vaz; MACHADO, Ana Flávia; DE CARVALHO, Lucas Resende. Music consumption in Brazil: an analysis of streaming reproductions. PragMATIZES - Revista Latino-Americana de Estudos em Cultura, v. 10, n. 19, p. 141-169, 2020. DOI: https://doi.org/10.22409/pragmatizes.v10i19.40565
GARIMELLA, Kiran; WEST, Robert. Hot streaks on social media. In: Proceedings of the AAAI International Conference on Web and Social Media. Palo Alto: AAAI Press, 2019. p. 170-180. DOI: https://doi.org/10.1609/icwsm.v13i01.3219
HENDRICKS, Darryll; PATEL, Jayendu; ZECKHAUSER, Richard. Hot hands in mutual funds: Short"run persistence of relative performance, 1974–1988. The Journal of finance, v. 48, n. 1, p. 93-130, 1993. DOI: https://doi.org/10.1111/j.1540-6261.1993.tb04703.x
HOLOPAINEN, Risto. Making Complex Music with Simple Algorithms, is it Even Possible?. Revista Vórtex, v. 9, n. 2, 2021. DOI: https://doi.org/10.33871/23179937.2021.9.2.3
JANOSOV, Milán; BATTISTON, Federico; SINATRA, Roberta. Success and luck in creative careers. EPJ Data Science, v. 9, n. 1, p. 9, 2020. DOI: https://doi.org/10.1140/epjds/s13688-020-00227-w
KEOGH, Eamonn J.; PAZZANI, Michael J. Scaling up dynamic time warping for datamining applications. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledege Discovery and Data Mining. New York: ACM, 2000. p. 285-289. DOI: https://doi.org/10.1145/347090.347153
KISCHINHEVSKY, Marcelo; VICENTE, Eduardo; DE MARCHI, Leonardo. Em busca da música infinita: os serviços de streaming e os conflitos de interesse no mercado de conteúdos digitais. Fronteiras-estudos midiáticos, v. 17, n. 3, p. 302-311, 2015. DOI: https://doi.org/10.4013/fem.2015.173.04
LIU, Lu et al. Hot streaks in artistic, cultural, and scientific careers. Nature, v. 559, n. 7714, p. 396-399, 2018. DOI: https://doi.org/10.1038/s41586-018-0315-8
LIU, Lu et al. Understanding the onset of hot streaks across artistic, cultural, and scientific careers. Nature Communications, v. 12, n. 1, p. 1-10, 2021. DOI: https://doi.org/10.1038/s41467-021-25477-8
MARTíN-GUTIÉRREZ, David et al. A multimodal end-to-end deep learning architecture for music popularity prediction. IEEE Access, v. 8, p. 39361-39374, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.2976033
OLIVEIRA, Gabriel P.; SILVA, Mariana O.; SEUFITELLI, Danilo B.; LACERDA, Anisio; MORO, Mirella M. Detecting collaboration profiles in success-based music genre networks. In: Proceedings of Int'l Society for Music Information Retrieval Conference (ISMIR), 21, 2020, Montreal, Canada. p. 726-732.
RAAB, Markus; GULA, Bartosz; GIGERENZER, Gerd. The hot hand exists in volleyball and is used for allocation decisions. Journal of Experimental Psychology: Applied, v. 18, n. 1, p. 81, 2012. DOI: https://doi.org/10.1037/a0025951
RABIN, Matthew; VAYANOS, Dimitri. The gambler's and hot-hand fallacies: Theory and applications. The Review of Economic Studies, v. 77, n. 2, p. 730-778, 2010. DOI: https://doi.org/10.1111/j.1467-937X.2009.00582.x
SANDRONI, Clara et al. A Covid-19 e seus efeitos na renda dos músicos brasileiros. Revista Vórtex, v. 9, n. 1, 2021. DOI: https://doi.org/10.33871/23179937.2021.9.1.7
SHINOHARA, Vítor; FOLEISS, Juliano; TAVARES, Tiago. Comparing Meta-Classifiers for Automatic Music Genre Classification. In: Proceedings of the 17th Brazilian Symposium on Computer Music. Porto Alegre: SBC, 2019. p. 131-135. DOI: https://doi.org/10.5753/sbcm.2019.10434
SILVA, Mariana O.; ROCHA, Laís M.; MORO, Mirella M. Collaboration profiles and their impact on musical success. In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. New York: ACM, 2019. p. 2070-2077. DOI: https://doi.org/10.1145/3297280.3297483
SINATRA, Roberta et al. Quantifying the evolution of individual scientific impact. Science, American Association for the Advancement of Science, v. 354, n. 6312, 2016. DOI: https://doi.org/10.1126/science.aaf5239
TAVENARD, Romain et al. Tslearn, a machine learning toolkit for time series data. J. Mach. Learn. Res., v. 21, n. 118, p. 1-6, 2020.
WALDFOGEL, Joel. How digitization has created a golden age of music, movies, books, and television. Journal of economic perspectives, v. 31, n. 3, p. 195-214, 2017. DOI: https://doi.org/10.1257/jep.31.3.195
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Danilo B. Seufitelli, Gabriel P. Oliveira, Mariana O. Silva, Gabriel R. G. Barbosa, Bruna C. Melo, Juliana E. Botelho, Luiza de Melo-Gomes, Mirella M. Moro
This work is licensed under a Creative Commons Attribution 4.0 International License.
Autores mantêm os direitos autorais e concedem à revista o direito de primeira publicação, com o trabalho simultaneamente licenciado sob a Licença Creative Commons Attribution que permite o compartilhamento do trabalho com reconhecimento da autoria e publicação inicial nesta revista.