# Interactive Machine Learning of Musical Gesture

arXiv: https://arxiv.org/abs/2011.13487 · HTML: https://mlsanity.com/paper/2011.13487/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Deep Neural Network approaches for Analysing Videos of Music Performances](https://mlsanity.com/paper/2205.11232/) (2022)
- [A Real-Time Gesture-Based Control Framework](https://mlsanity.com/paper/2504.19460/) (2025)

## Abstract (arXiv)

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.