Analysis of Noisy Biosignals for Musical Performance
Biosignal sensors are now small, affordable, and wireless. We desire to include such sensors (e.g. heart rate, respiration, acceleration) in a live musical performance, which sets requirements on the reliability and variability of the data. Unfortunately the raw signals from such devices are unable to meet these requirements. We contribute our solutions for overcoming the shortcomings of these sensors in two parts. The first is an online data processing and analysis system, including on-line generative models that describe the signals but add consistency. The second is the end-to-end system for capturing wireless signal data for the analysis system and integrating the resulting output into a popular digital audio workstation in a very flexible manner conducive to live performance. We also explore the role of “analysis supervisor”—a member of the performing act who ensures that the results of biosignal analysis fall within the desired ranges to contribute to the music effectively.
KeywordsECG respiration accelerometer music performance
Unable to display preview. Download preview PDF.
- 1.Henry, T.K.: Invention locates hurt brain cells. New York Times, 21 (March 2, 1943)Google Scholar
- 2.Lucier, A.: Statement on: Music for solo performer. In: Rosenboom, D. (ed.) Biofeedback and the Arts, Results of Early Experiments. Aesthetic Research Centre of Canada Publications (1976)Google Scholar
- 3.Teitelbaum, R.: In tune: Some early experiments in biofeedback music. In: Rosenboom, D. (ed.) Biofeedback and the Arts, Results of Early Experiments. Aesthetic Research Centre of Canada Publications (1976)Google Scholar
- 4.Eaton, M.L.: Bio-music: biological feedback experimental music systems. Something Else Press (1971)Google Scholar
- 5.Ojanen, M., Suominen, J., Kallio, T., Lassfolk, K.: Design principles and user interfaces of Erkki Kurenniemi’s electronic musical instruments of the 1960’s and 1970’s. In: NIME 2007: Proceedings of the International Conference on New Interfaces for Musical Expression, pp. 88–93 (2007)Google Scholar
- 6.Rosenboom, D.: Extended musical interface with the human nervous system. Leonardo Monograph Series, vol. 1. International Society for the Arts, Sciences and Technology (1990)Google Scholar
- 8.Knapp, R., Jaimovich, J., Coghlan, N.: Measurement of motion and emotion during musical performance. In: ACII 2009: 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, pp. 1–5 (2009)Google Scholar
- 9.Le Groux, S., Manzolli, J., Verschure, P.F.M.J.: Disembodied and collaborative musical interaction in the multimodal brain orchestra. In: NIME 2010: Proceedings of the International Conference on New Interfaces for Musical Expression, pp. 309–314 (2010)Google Scholar
- 11.Mann, S., Fung, J., Garten, A.: DECONcert: bathing in the light, sound, and waters of the musical brainbaths. In: ICMC 2007: International Computer Music Conference (2007)Google Scholar
- 16.Freed, A., Schmeder, A.: Features and future of Open Sound Control version 1.1 for NIME. In: NIME 2009: Proceedings of the International Conference on New Interfaces for Musical Expression, pp. 116–120 (2009)Google Scholar