User Curated Shaping of Expressive Performances

Research output: Chapter in Book/Report/Conference proceedingConference proceedingspeer-review

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.
Original languageEnglish
Title of host publicationIn ICML 2019 Workshop on Machine Learning for Music Discovery, 36th International Conference on Machine Learning (ICML 2019)
Number of pages4
Publication statusPublished - Jun 2019

Fields of science

  • 202002 Audiovisual media
  • 102 Computer Sciences
  • 102001 Artificial intelligence
  • 102003 Image processing
  • 102015 Information systems

JKU Focus areas

  • Digital Transformation

Cite this