Interactive Machine Learning: Strategies for live performance using Electromyography

Tools

Zbyszynski, Michael; Tanaka, Atau and Visi, Federico. 2020. Interactive Machine Learning: Strategies for live performance using Electromyography. In: Hugo Silva, ed. Open Source Biomedical Engineering. Springer. [Book Section] (Forthcoming)

No full text available

Text
IML_EMG_Book_Chapter.pdf - Draft Version
Permissions: Administrator Access Only
Available under License Creative Commons Attribution Non-commercial.
Download (3MB)

Abstract or Description

We present use of the electromyogram (EMG) for sensing musical gesture and discuss interactive machine learning for designing complex muscle-music inter- actions. We propose a signal flow for musical interaction with body movement sensed by EMG and other sensing modalities, feature extraction, and interactive machine learning that result in the manipulation of sound synthesis parame- ters. We discuss ways to capture the EMG for musical use including electrode placement and the use of multiple EMG channels. Techniques for extracting meaningful data and features from electromyographic signals are discussed. We present a composite feature, a multi-channel EMG vector sum. Signal pro- cessing and machine learning are demonstrated. We frame classification and regression as cases of recognising and mapping. We finish by providing a series of resources for musicians wishing to work with the techniques presented here. These tools are free and open source, and can be implemented easily in new applications, projects or products.

Item Type:

Book Section

Additional Information:

The research leading to these results has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme ( BioMusical Instruments, Grant agreement No. 789825) and was supported in part by RAPID-MIX, an EU Horizon 2020 Innovation Action: H2020-ICT-2014-1 Project ID: 644862.