Violin makers and musicians describe the timbral qualities of violins using semantic terms coming from natural language. In this study we use regression techniques of machine intelligence and audio features to model in a training-based fashion a set of high-level (semantic) descriptors for the automatic annotation of musical instruments. The most relevant semantic descriptors are collected through interviews to violin makers. These descriptors are then correlated with objective features extracted from a set of violins from the historical and contemporary collections of the Museo del Violino and of the International School of Luthiery both in Cremona. As sound description can vary throughout a performance, our approach also enables the modeling of time-varying (evolutive) semantic annotations.

Authors: Zanoni, Massimiliano; Setragno, Francesco; Antonacci, Fabio; Sarti, Augusto; Fazekas, György; Sandler, Mark B.
Affiliations: Politecnico di Milano, Milan, Italy; Queen Mary University of London, London, UK(See document for exact affiliation information.)
AES Convention: 138 (May 2015) Paper Number: 9353
Publication Date: May 6, 2015
Subject: Semantic Audio

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