Stompboxes: Kicking the Habit
Gregory Burlet, and Ichiro Fujinaga
Proceedings of the International Conference on New Interfaces for Musical Expression
- Year: 2013
- Location: Daejeon, Republic of Korea
- Pages: 41–44
- Keywords: Augmented instrument, gesture recognition, accelerometer, pattern recognition, performance practice
- DOI: 10.5281/zenodo.1178488 (Link to paper)
- PDF link
Abstract:
Sensor-based gesture recognition is investigated as a possible solution to theproblem of managing an overwhelming number of audio effects in live guitarperformances. A realtime gesture recognition system, which automaticallytoggles digital audio effects according to gestural information captured by anaccelerometer attached to the body of a guitar, is presented. To supplement theseveral predefined gestures provided by the recognition system, personalizedgestures may be trained by the user. Upon successful recognition of a gesture,the corresponding audio effects are applied to the guitar signal and visualfeedback is provided to the user. An evaluation of the system yielded 86%accuracy for user-independent recognition and 99% accuracy for user-dependentrecognition, on average.
Citation:
Gregory Burlet, and Ichiro Fujinaga. 2013. Stompboxes: Kicking the Habit. Proceedings of the International Conference on New Interfaces for Musical Expression. DOI: 10.5281/zenodo.1178488BibTeX Entry:
@inproceedings{Burlet2013, abstract = {Sensor-based gesture recognition is investigated as a possible solution to theproblem of managing an overwhelming number of audio effects in live guitarperformances. A realtime gesture recognition system, which automaticallytoggles digital audio effects according to gestural information captured by anaccelerometer attached to the body of a guitar, is presented. To supplement theseveral predefined gestures provided by the recognition system, personalizedgestures may be trained by the user. Upon successful recognition of a gesture,the corresponding audio effects are applied to the guitar signal and visualfeedback is provided to the user. An evaluation of the system yielded 86%accuracy for user-independent recognition and 99% accuracy for user-dependentrecognition, on average.}, address = {Daejeon, Republic of Korea}, author = {Gregory Burlet and Ichiro Fujinaga}, booktitle = {Proceedings of the International Conference on New Interfaces for Musical Expression}, doi = {10.5281/zenodo.1178488}, issn = {2220-4806}, keywords = {Augmented instrument, gesture recognition, accelerometer, pattern recognition, performance practice}, month = {May}, pages = {41--44}, publisher = {Graduate School of Culture Technology, KAIST}, title = {Stompboxes: Kicking the Habit}, url = {http://www.nime.org/proceedings/2013/nime2013_109.pdf}, year = {2013} }