Mining Exceptional Genre Patterns on Hit Songs

Resumo


The music industry has always been complex and competitive. Nowadays, combining different genres has become a common practice to promote new music and reach new audiences. Given the diversity of combinations between all genres, predictive and descriptive analyses are very challenging. Here, our goal is to mine frequent and exceptional patterns in music collaborations that have achieved success in both global and regional markets. We use the Apriori algorithm to mine genre patterns and association rules that reveal how music genres combine with each other in each market. The results show significant differences in the behavior of each market and a strong influence of the regional factor on musical success. In addition, we are able to use such patterns to identify and recommend promising genre combinations for such markets through the association rules.

Palavras-chave: hit song science, music data mining, music information retrieval, musical genres

Referências

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

Agrawal, R. et al. Fast algorithms for mining association rules. In VLDB. Vol. 1215. pp. 487–499, 1994.

Calefato, F. et al. Collaboration success factors in an online music community. In ACM GROUP. Sanibel Island, USA, 2018.

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

Dhanaraj, R. and Logan, B. Automatic prediction of hit songs. In ISMIR. pp. 488–491, 2005.

Fontes, S. G. et al. Association rules mining applied in the animal movement exploratory analysis. In KDMiLe. SBC, pp. 1–8, 2019.

Gienapp, L. et al. Topological properties of music collaboration networks: The case of jazz and hip hop. Digit. Humanit. Q. 15 (1), 2021.

Iloga, S. et al. A sequential pattern mining approach to design taxonomies for hierarchical music genre recognition. Pattern Anal. Appl. 21 (2): 363–380, 2018.

Mayerl, M. et al. Pairwise learning to rank for hit song prediction. In BTW. LNI, vol. P-331. Gesellschaft für Informatik e.V., pp. 555–565, 2023.

Melo, E. et al. Combining Data Mining Techniques to Analyse Factors Associated with Allocation of Socioeconomic Resources at IFMG. In KDMiLe. SBC, pp. 89–96, 2021.

Oliveira, G. P. et al. Detecting collaboration profiles in success-based music genre networks. In ISMIR. pp. 726–732, 2020.

Ordanini, A. et al. The featuring phenomenon in music: how combining artists of different genres increases a song’s popularity. Market. Letters vol. 29, pp. 485–499, 2018.

Ren, J. and Kauffman, R. J. Understanding music track popularity in a social network. In ECIS. AIS, Atlanta, GA, USA, pp. 374–388, 2017.

Rompré, L. et al. Using association rules mining for retrieving genre-specific music files. In FLAIRS Conference. AAAI Press, pp. 706–711, 2017.

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

Silva, M. O. et al. Collaboration as a driving factor for hit song classification. In WebMedia. ACM, pp. 66–74, 2022.

Zaki, M. J. and Meira Jr., W. Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, 2014.

Zangerle, E. et al. Hit song prediction: Leveraging low- and high-level audio features. In ISMIR. pp. 319–326, 2019.
Publicado
26/09/2023
Como Citar

Selecione um Formato
OLIVEIRA, Gabriel P.; MORO, Mirella M.. Mining Exceptional Genre Patterns on Hit Songs. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 11. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 97-104. ISSN 2763-8944. DOI: https://doi.org/10.5753/kdmile.2023.232412.