MUHSIC: An Open Dataset with Temporal Musical Success Information
Resumo
Music is an alive industry with an increasing volume of complex data that creates new challenges and opportunities for extracting knowledge, benefiting not only the different music segments but also the Music Information Retrieval (MIR) community. In this paper, we present MUHSIC, a novel dataset with enhanced information on musical success. We focus on artists and genres by combining chart-related data with acoustic metadata to describe the temporal evolution of musical careers. The enriched and curated data allow building success-based time series to investigate high-impact periods (hot streaks) in such careers, transforming complex data into knowledge. Overall, MUHSIC is a relevant tool in music-related tasks due to its easy use and replicability.
Palavras-chave:
Dataset, Musical Success, Hot streak
Referências
Aggarwal, C. C. (2016). Recommender Systems - The Textbook. Springer. doi:10.1007/978-3-319-29659-3.
Bertin-Mahieux, T. et al. (2011). The Million Song Dataset. In Proc. of Int’l Society for Music Information Retrieval Conf. (ISMIR), pages 591–596.
Byrd, D. and Crawford, T. (2002). Problems of music information retrieval in the real world. Information Processing & Management, 38(2):249–272. doi:10.1016/S0306-4573(01)00033-4.
Cosimato, A. et al. (2019). The conundrum of success in music: Playing it or talking about it? IEEE Access, 7:123289–123298. doi:10.1109/ACCESS.2019.2937743
Çimen, A. and Kayis, E. (2021). A longitudinal model for song popularity prediction. In DATA, pages 96–104. SciTePress. doi:10.5220/0010607700960104
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., Battiston, F., and Sinatra, R. (2020). Success and luck in creative careers. EPJ Data Sci., 9(1):9. doi:10.1140/epjds/s13688-020-00227-w.
Karydis, I., Gkiokas, A., and Katsouros, V. (2016). Musical track popularity mining dataset. In IFIP AIAI, pages 562–572. doi:10.1007/978-3-319-44944-9_50
Keogh, E. J. and Pazzani, M. J. (2000). Scaling up dynamic time warping for datamining applications. In ACM SIGKDD, pages 285–289. ACM. doi:10.1145/347090.347153
Liu, L., Wang, Y., Sinatra, R., Giles, C. L., Song, C., and Wang, D. (2018). Hot streaks in artistic, cultural, and scientific careers. Nature, 559(7714):396–399. doi:10.1038/s41586-018-0315-8
Melchiorre, A. B. et al. (2021). Investigating gender fairness of recommendation algorithms in the music domain. Information Processing & Management, 58(5):102666. doi:10.1016/j.ipm.2021.102666
Oliveira, G. P. (2021). Analyses of musical success based on time, genre and collaboration. Master’s thesis, Universidade Federal de Minas Gerais, Brazil.
Oliveira, G. P., Barbosa, G. R. G., Melo, B. C., Silva, M. O., Seufitelli, D. B., Lacerda, A., and Moro, M. M. (2021). MUHSIC: An Open Dataset with Temporal Musical Success Information. Zenodo. doi:10.5281/zenodo.5168695.
Oliveira, G. P., Silva, M. O., Seufitelli, D. B., Lacerda, A., and Moro, M. M. (2020). Detecting collaboration profiles in success-based music genre networks. In Procs. Int’l Society for Music Information Retrieval Conference (ISMIR), Montreal, Canada.
Pachet, F. (2011). Hit song science. In Tao Li, Mitsunori Ogihara, G. T., editor, Music Data Mining, chapter 10, pages 305–326. CRC Press, New York, NY, USA.
Scaringella, N., Zoia, G., and Mlynek, D. (2006). Automatic genre classification of music content: a survey. IEEE Signal Process. Mag., 23(2):133–141. doi:10.1109/MSP.2006.1598089.
Silva, M. O., Rocha, L. M., and Moro, M. M. (2019). MusicOSet: An Enhanced Open Dataset for Music Data Mining. In SBBD DSW, pages 408–417. SBC.
Zangerle, E., Huber, R., and Yang, M. V. Y.-H. (2019). Hit Song Prediction: Leveraging Low- and High-Level Audio Features. In Proc. of Int’l Society for Music Information Retrieval Conf. (ISMIR), pages 319–326.
Bertin-Mahieux, T. et al. (2011). The Million Song Dataset. In Proc. of Int’l Society for Music Information Retrieval Conf. (ISMIR), pages 591–596.
Byrd, D. and Crawford, T. (2002). Problems of music information retrieval in the real world. Information Processing & Management, 38(2):249–272. doi:10.1016/S0306-4573(01)00033-4.
Cosimato, A. et al. (2019). The conundrum of success in music: Playing it or talking about it? IEEE Access, 7:123289–123298. doi:10.1109/ACCESS.2019.2937743
Çimen, A. and Kayis, E. (2021). A longitudinal model for song popularity prediction. In DATA, pages 96–104. SciTePress. doi:10.5220/0010607700960104
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., Battiston, F., and Sinatra, R. (2020). Success and luck in creative careers. EPJ Data Sci., 9(1):9. doi:10.1140/epjds/s13688-020-00227-w.
Karydis, I., Gkiokas, A., and Katsouros, V. (2016). Musical track popularity mining dataset. In IFIP AIAI, pages 562–572. doi:10.1007/978-3-319-44944-9_50
Keogh, E. J. and Pazzani, M. J. (2000). Scaling up dynamic time warping for datamining applications. In ACM SIGKDD, pages 285–289. ACM. doi:10.1145/347090.347153
Liu, L., Wang, Y., Sinatra, R., Giles, C. L., Song, C., and Wang, D. (2018). Hot streaks in artistic, cultural, and scientific careers. Nature, 559(7714):396–399. doi:10.1038/s41586-018-0315-8
Melchiorre, A. B. et al. (2021). Investigating gender fairness of recommendation algorithms in the music domain. Information Processing & Management, 58(5):102666. doi:10.1016/j.ipm.2021.102666
Oliveira, G. P. (2021). Analyses of musical success based on time, genre and collaboration. Master’s thesis, Universidade Federal de Minas Gerais, Brazil.
Oliveira, G. P., Barbosa, G. R. G., Melo, B. C., Silva, M. O., Seufitelli, D. B., Lacerda, A., and Moro, M. M. (2021). MUHSIC: An Open Dataset with Temporal Musical Success Information. Zenodo. doi:10.5281/zenodo.5168695.
Oliveira, G. P., Silva, M. O., Seufitelli, D. B., Lacerda, A., and Moro, M. M. (2020). Detecting collaboration profiles in success-based music genre networks. In Procs. Int’l Society for Music Information Retrieval Conference (ISMIR), Montreal, Canada.
Pachet, F. (2011). Hit song science. In Tao Li, Mitsunori Ogihara, G. T., editor, Music Data Mining, chapter 10, pages 305–326. CRC Press, New York, NY, USA.
Scaringella, N., Zoia, G., and Mlynek, D. (2006). Automatic genre classification of music content: a survey. IEEE Signal Process. Mag., 23(2):133–141. doi:10.1109/MSP.2006.1598089.
Silva, M. O., Rocha, L. M., and Moro, M. M. (2019). MusicOSet: An Enhanced Open Dataset for Music Data Mining. In SBBD DSW, pages 408–417. SBC.
Zangerle, E., Huber, R., and Yang, M. V. Y.-H. (2019). Hit Song Prediction: Leveraging Low- and High-Level Audio Features. In Proc. of Int’l Society for Music Information Retrieval Conf. (ISMIR), pages 319–326.
Publicado
04/10/2021
Como Citar
OLIVEIRA, Gabriel P.; BARBOSA, Gabriel R. G.; MELO, Bruna C.; SILVA, Mariana O.; SEUFITELLI, Danilo B.; MORO, Mirella M..
MUHSIC: An Open Dataset with Temporal Musical Success Information. In: DATASET SHOWCASE WORKSHOP (DSW), 3. , 2021, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 65-76.
DOI: https://doi.org/10.5753/dsw.2021.17415.
