Datasets for the Analysis of Expressive Musical Gestures

Abstract

In this paper we present two datasets of instrumental gestures performed with expressive variations: five violinists performing standard pedagogical phrases with variation in dynamics and tempo; and two pianists performing a repertoire piece with variations in tempo, dynamics and articulation. We show the utility of these datasets by highlighting the different movement qualities embedded in both datasets. In addition, for the violin dataset, we report on gesture recognition tests using two state-of-the-art realtime gesture recognizers. We believe that these resources create opportunities for further research on the understanding of complex human movements through computational methods.