A cluster analysis of benchmark acoustic features on Brazilian music

  • Leonardo Ferreira University Center of FEI
  • Estela Ribeiro University Center of FEI
  • Carlos Thomaz University Center of FEI


In this work, we extend a standard and successful acoustic feature extraction approach based on trigger selection to examples of Brazilian Bossa-Nova and Heitor Villa Lobos music pieces. Additionally, we propose and implement a computational framework to disclose whether all the acoustic features extracted are statistically relevant, that is, non-redundant. Our experimental results show that not all these well-known features might be necessary for trigger selection, given the multivariate statistical redundancy found, which associated all these acoustic features into 3 clusters with different factor loadings and, consequently, representatives.

Palavras-chave: Brain-Computer Interfaces and Physiological Signals, Music Information Retrieval, Music Perception, Psychoacoustics and Cognition


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FERREIRA, Leonardo; RIBEIRO, Estela; THOMAZ, Carlos. A cluster analysis of benchmark acoustic features on Brazilian music. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO MUSICAL (SBCM), 17. , 2019, São João del-Rei. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 194-196. DOI: https://doi.org/10.5753/sbcm.2019.10444.