An unsupervised approach to the semantic description of the sound quality of violins
Michele Buccoli, Massimiliano Zanoni, Francesco Setragno, Fabio Antonacci, Augusto Sarti · 2015
In this study we propose a set of semantic musical descriptors that can be used for describing the timbre of violins. The proposed semantic model follows a dimensional approach, which allows us to express the degree of intensity of each descriptor. A set of recordings of a number of violins (among them, Stradivari, Amati and Guarnieri instruments) were annotated with the descriptors through questionnaires. The recordings are processed with deep learning techniques, to learn salient features from the audio signal in an unsupervised fashion. In this study we propose an automatic annotation procedure based on a set of regression functions that model each semantic descriptor using the learned set of features.