交互式机器学习音乐手术 (Interactive Machine Learning of Musical Gesture)

from arxiv, Author's accepted manuscript, to appear as a chapter in "Handbook of Artificial Intelligence for Music: Foundations, Advanced Approaches, and Developments for Creativity", edited by E. R. Miranda. Cham: Springer Nature, 2021

This chapter presents an overview of Interactive Machine Learning (IML) techniques applied to the analysis and design of musical gestures. We go through the main challenges and needs related to capturing, analysing, and applying IML techniques to human bodily gestures with the purpose of performing with sound synthesis systems. We discuss how different algorithms may be used to accomplish different tasks, including interacting with complex synthesis techniques and exploring interaction possibilities by means of Reinforcement Learning (RL) in an interaction paradigm we developed called Assisted Interactive Machine Learning (AIML). We conclude the chapter with a description of how some of these techniques were employed by the authors for the development of four musical pieces, thus outlining the implications that IML have for musical practice.

翻译：本章概述用于分析和设计音乐手势的交互式机器学习技术(IML),我们探讨与捕捉、分析和将IML技术应用于人体手势有关的主要挑战和需要,目的是用健全的合成系统进行演练,我们讨论如何使用不同的算法完成不同的任务,包括与复杂的合成技术互动,并探讨通过在我们开发的称为辅助互动机学习的互动模式中加强学习(RL)的互动可能性。我们最后一章说明这些技术中的一些技术是如何由作者用来开发四种音乐作品的,从而概述IML对音乐实践的影响。