Temporal Analysis and Visualisation of Music


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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MISAEL, Luan; FORSTER, Carlos; FONTELLES, Emanuel; SAMPAIO, Vinicius; FRANÇA, Mardônio. Temporal Analysis and Visualisation of Music. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 17. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 507-518. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2020.12155.