A cluster analysis of benchmark acoustic features on Brazilian music - Ferreira, Leonardo; Ribeiro, Estela; Thomaz, Carlos (Anais do Simpósio Brasileiro de Computação Musical (SBCM); 2019: Anais do XVII Simpósio Brasileiro de Computação MusicalDO - 10.5753/sbcm.2019.10444, 2019)
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.