Artificial neural networks based models for automatic performance of musical scores
Roberto Bresin · Journal of New Music Research · 1998
This article briefly summarises the author's research on automatic performance, started at CSC (Centro di Sonologia Computazionale, University of Padua) and continued at TMH‐KTH (Speech, Music Hearing Department at the Royal Institute of Technology, Stockholm). The focus is on the evolution of the architecture of an artificial neural networks (ANNs) framework, from the first simple model, able to learn the KTH performance rules, to the final one, that accurately simulates the style of a real pianist performer, including time and loudness deviations. The task was to analyse and synthesise the performance process of a professional pianist, playing on a Disklavier. An automatic analysis extracts all performance parameters of the pianist, starting from the KTH rule system. The system possesses good generalisation properties: applying the same ANN, it is possible to perform different scores in the performing style used for the training of the networks. Brief descriptions of the program Melodia and of the two Java applets Japer and Jalisper are given in the Appendix. In Melodia, developed at the CSC, the user can run either rules or ANNs, and study their different effects. Japer and Jalisper, developed at TMH, implement in real time on the web the performance rules developed at TMH plus new features achieved by using ANNs.