Modeling Moods in Violin Performances

TitleModeling Moods in Violin Performances
Publication TypeConference Paper
Year of Publication2008
Conference NameSound and Music Computing Conference
AuthorsPérez, A., Ramirez R., & Kersten S.
AbstractIn this paper we present a method to model and compare expressivity for different Moods in violin performances. Models are based on analysis of audio and bowing control gestures of real performances and they predict expressive scores from non expressive ones. Audio and control data is captured by means of a violin pickup and a 3D motion tracking system and aligned with the performed score. We make use of machine learning techniques in order to extract expressivity rules from score-performance deviations. The induced rules conform a generative model that can transform an inexpressive score into an expressive one.
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