Temporal Analysis and Visualisation of Music

• Luan Misael Itaú Unibanco http://orcid.org/0000-0003-4685-3378
• Carlos Forster ITA http://orcid.org/0000-0003-3390-1051
• Emanuel Fontelles Universidade Federal do Ceará http://orcid.org/0000-0001-7622-0243
• Vinicius Sampaio UECE http://orcid.org/0000-0003-1603-2640
• Mardônio França Boitatá LAB http://orcid.org/0000-0003-1876-9670

DOI: https://doi.org/10.5753/eniac.2020.12155

Resumo

This paper proposes a temporal analysis for music metadata using a generative probabilistic model for collections the discrete datasets such as text corpora. This method is also a topic model that is used for discovering abstract topics from a collection of documents. The method is then applied to audio metadata and song lyrics extracted with Echo Nest® engine, Spotify® Lyrics Genius® API. Song data time series are generated by grouping data items by release date, genre and dominant topics (from LDA analysis). Using a technique from Network Theory we visualise how these topics, in this case, genres, are related to each other through time.

Palavras-chave: music, topic modelling, lda, spotify

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