Collaboration-Aware Hit Song Analysis and Prediction

  • Mariana O. Silva UFMG
  • Mirella M. Moro UFMG

Resumo


We propose tackling the Hit Song Prediction problem through a multimodal form with songs’ features fused together. Specifically, we describe songs from three feature modalities: music, artist and album. Initially, we identify collaboration profiles in a success-based musical network, unveiling how professional connections can significantly impact their success. Then, we use time series and the Granger Causality test for assessing whether there is a causal relationship between collaboration profiles and artists’ popularity. Finally, we model the Hit Song Prediction problem as two distinct tasks: classification and placement. The former is a classical binary classification model and directly applies our fusion strategies. The latter is a modeling approach that ranks a song relative to a given chart, predicts hit songs, and provides comparative popularity information of a set of songs. Furthermore, we emphasize collaboration artists’ profiles as important features when describing their songs. Overall, our empirical studies confirm the effectiveness of our method that fuses heterogeneous data for both tasks.

Palavras-chave: Collaboration Profiles, Hit Song Science, Musical Success, Social Networks, Machine Learning, Multimodal

Referências

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Publicado
05/11/2021
SILVA, Mariana O.; MORO, Mirella M.. Collaboration-Aware Hit Song Analysis and Prediction. In: CONCURSO DE TESES E DISSERTAÇÕES - SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 27. , 2021, Minas Gerais. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 11-14. ISSN 2596-1683. DOI: https://doi.org/10.5753/webmedia_estendido.2021.17603.