Implementation of Expressive Performance Rules on the WF-4RIII by modeling a professional flutist performance using NN

In this paper, the methodology for automatically generating an expressive performance on the anthropomorphic flutist robot is detailed. A feed-forward network trained with the error back-propagation algorithm was implemented to model the performance´s expressiveness of a professional flutist. In particular, the note duration and vibrato were considered as performance rules (sources of variation) to enhance the robot´s performance expressiveness. From the mechanical point of view, the vibrato and lung systems were re-designed to effectively control the proposed music performance rules. An experimental setup was proposed to verify the effectiveness of generating a new score with expressiveness from a model created based on the performance of a professional flutist. As a result, the flutist robot was able of automatically producing an expressive performance similar to the human one from a nominal score.