User Curated Shaping of Expressive Performances
This work addresses the challenge of interpreting expressive musical performances for musicians and researchers, though it is incremental as it builds on existing basis function models.
The paper tackles the problem of understanding how musicians create expressive performances by manipulating parameters like dynamics and tempo, presenting an interactive interface that allows users to explore the relationship between score features and these parameters, with results demonstrated through a data-driven framework using neural networks.
Musicians produce individualized, expressive performances by manipulating parameters such as dynamics, tempo and articulation. This manipulation of expressive parameters is informed by elements of score information such as pitch, meter, and tempo and dynamics markings (among others). In this paper we present an interactive interface that gives users the opportunity to explore the relationship between structural elements of a score and expressive parameters. This interface draws on the basis function models, a data-driven framework for expressive performance. In this framework, expressive parameters are modeled as a function of score features, i.e., numerical encodings of specific aspects of a musical score, using neural networks. With the proposed interface, users are able to weight the contribution of individual score features and understand how an expressive performance is constructed.