Training expressive performance by means of visual feedback: Existing and potential applications of performance measurement techniques
Oxford : Oxford University Press
InFabian, D.; Timmers, R.; Schubert, E. (ed.), Expressiveness in music performance: Empirical approaches across styles and cultures, pp. 304-327
Part of book or chapter of book
Display more detailsDisplay less details
SW OZ DCC AI
Fabian, D.; Timmers, R.; Schubert, E. (ed.), Expressiveness in music performance: Empirical approaches across styles and cultures
SubjectCognitive artificial intelligence; DI-BCB_DCC_Theme 4: Brain Networks and Neuronal Communication
This chapter explores current use of automated feedback techniques among musicians, and the anticipated usefulness of such systems. It examines purpose-designed software available for performers, and then summarizes the results of experimental investigations of the effectiveness of feedback systems in enhancing practicing to perform musical excerpts in various expressive manners. The methodological challenges of designing a program that can be applied in a general manner without biasing practice and performance are discussed. Promising avenues are suggested - for example, by making feedback summative rather than real-time, and based on probabilistic learning from target examples. In addition, the training may be to widely explore performance expression rather than to reinforce through imitation. The survey discussed indicates that if a user-friendly, reliable, and non-biasing product is realized, it is very likely to be adopted for a multitude of reasons, including feedback on ensemble timing, expressive interpretation, and aspects of performance control.
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.