Open AccessTemporal Analysis and Visualisation of Music
Author(s) -
Luan Misael,
Carlos Henrique Quartucci Forster,
Emanuel Fontelles,
Vinicius Sampaio,
Mardônio França
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/eniac.2020.12155
Subject(s) - metadata , computer science , lyrics , probabilistic logic , information retrieval , generative model , generative grammar , visualization , natural language processing , artificial intelligence , world wide web , art , literature
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.