Abstract
In recent years, wearable sensors have allowed us to utilise previously inaccessible forms of biometric data for interactive music composition and live music performances.
In particular, data which measures muscle tension (Electromyographic - EMG). EMG data is interesting to use because it allows for better gestural control when generating a desired sonic output (via Digital Signal Processing - DSP), in comparison to other datasets, such as Electroencephalography (EEG).
New research enquiries for music composition in different modalities (physical and digital spaces) can thus be made as a result of this improved gestural control.
In particular, data which measures muscle tension (Electromyographic - EMG). EMG data is interesting to use because it allows for better gestural control when generating a desired sonic output (via Digital Signal Processing - DSP), in comparison to other datasets, such as Electroencephalography (EEG).
New research enquiries for music composition in different modalities (physical and digital spaces) can thus be made as a result of this improved gestural control.
| Original language | English |
|---|---|
| Pages | 1 |
| Number of pages | 1 |
| Publication status | Accepted/In press - 20 Nov 2019 |
| Event | BEYOND19 - UK, Edinburgh, United Kingdom Duration: 20 Nov 2019 → 21 Nov 2019 https://beyondconference.org/ |
Conference
| Conference | BEYOND19 |
|---|---|
| Abbreviated title | BEYOND |
| Country/Territory | United Kingdom |
| City | Edinburgh |
| Period | 20/11/19 → 21/11/19 |
| Internet address |
Keywords
- Music
- Composition
- Machine
- Learning
- Wekinator
- Myo
- Biometric
- Data
- Sonification