MGD+: An Enhanced Music Genre Dataset with Success-based Networks

• Danilo B. Seufitelli Universidade Federal de Minas Gerais http://orcid.org/0000-0002-0155-7631
• Gabriel P. Oliveira Universidade Federal de Minas Gerais https://orcid.org/0000-0002-7210-6408
• Mariana O. Silva Universidade Federal de Minas Gerais
• Mirella M. Moro Universidade Federal de Minas Gerais https://orcid.org/0000-0002-0545-2001

DOI: https://doi.org/10.5753/dsw.2023.233826

Resumo

Streaming platforms like Spotify have revolutionized music consumption, generating big volumes of data on hit songs. Such data serve as input to analyzing the music community and to the field of Music Information Retrieval. In this context, we present MGD+: an enhanced Music Genre Dataset with Success-based Networks. By combining Spotify chart data with acoustic metadata, we capture the evolution of musical careers. We further enhance the dataset with a genre-based collaboration network, represented as a graph, connecting artists through collaborations. MGD+ enables building success-based time series across several music markets, offers a friendly interface, and allows reproducibility; being a valuable tool for music-related tasks.

Palavras-chave: Hit song science, Music genres, Success networks

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