English

User Curated Shaping of Expressive Performances

Sound 2019-06-18 v1 Human-Computer Interaction Machine Learning Audio and Speech Processing

Abstract

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.

Keywords

Cite

@article{arxiv.1906.06428,
  title  = {User Curated Shaping of Expressive Performances},
  author = {Zhengshan Shi and Carlos Cancino-Chacón and Gerhard Widmer},
  journal= {arXiv preprint arXiv:1906.06428},
  year   = {2019}
}

Comments

4 pages, ICML 2019 Machine Learning for Music Discovery Workshop

R2 v1 2026-06-23T09:54:20.090Z