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Agent-based guitar performance simulation
Journal of the Brazilian Computer Society volume 14, pages 19–29 (2008)
The goal of this paper is to describe a systemaided performance and composition tool that aims to expand guitarist capacities by providing innovative ways in which the user can interact with the system. In order to achieve that, we decided to use an agentbased approach, independently modeling the active elements involved in a guitar performance as autonomous agents — named Left-Hand, Right-Hand, and Speaker (the guitar itself). These agents are able to communicate to each other in order to make some musical decisions, specially related to the chord’s shape choice. The musical elements (harmony and rhythm) are independently defined respectively by the Left-Hand and Right-Hand agents. The most relevant aspects of this work, however, are the algorithms and strategies to process both harmonic and rhythmic data. Finally, we perform an evaluation of the system and discuss the results of the implemented techniques.
Costalonga, L. and Miranda, E., Equipping Artificial Guitar Players with Biomechanical Constrains: A Case Study of Precision and Speed,in Proc. International Computer Music Conference, Belfast, 2008 (to appear)
Costalonga, L.; Viccari, R. M.; Multiagent System for Guitar Rhythm Simulation.In Proceedings of the International Conference on Computing, Communications and Control Technologies, Austin, Texas, USA, 2004.
Fagundes, M. S, Vicari, R. M. and Coelho, H. (2007) Deliberation Process in a BDI Model with Bayesian Networks.In: 10th Pacific RIM International Workshop on Multi-Agents (PRIMA 2007). Lecture Notes in Artificial Intelligence. 2007.
Gabrielsoon, A., Music Performance. The Psychology of Music.In.D. Deutsh, ed. The Psychology of Music, 2nd ed. New York: Academic Press, 1997.
Heijink, H. and Meulenbroek, R.G. On the complexity of classical guitar playing: functional adaptations to task constraints, J Mot Behav, 34 (4) 339–351, 2002.
Honing, H. Computational Modeling of Music Cognition: A Case Study on Model Selection, Univ. of California Press, 2004.
Music- Computer music composition in Java . Available at http://jmusic.ci.qut.edu.au/. Accessed on Jan 2008.
Miranda, E. R., An Artificial Intelligence Approach to Sound Design.Computer Music Journal 19(2), 1995, pp. 59–74.
Miranda, E. R. Emergent Sound Repertories in Virtual Societies.Computer Music Journal, 26(2), Cambridge, Massachussetts: MIT Press, 2002, pp. 77–90.
Radicioni, D. and V. Lombardo “Guitar Fingering for Music Performance.”Proc. International Computer Music Conference: 527–530, 2005.
Wessel, D. & Wright, M. Problems and Prospects for Intimate Musical Control of Computers.Computer Music Journal, 26(3), Cambridge, Massachussetts: MIT Press, 2002, pp. 11–22.
West, R., Howell, P., Cross, I.. Musical Structure and Knowledge Representation.In P. Howell, R. West, & I. Cross (Eds.),Representing Musical Structure (pp.1–30). London: Academic Press, 1991.
Wing, A. M.,P. Haggard, et al. Hand and brain: the neurophysiology and psychology of hand movements, Academic Press San Diego, 1996.
Wulfhorst, R.; Nakayama, L.; Vicari, R. M. A Multiagent Approach for Musical Interactive Systems.In Proceedings of the Second International joint Conference on Autonomous Agents and Multiagent Systems, Melbourne. 2003
Zhang, Q. and Miranda, E. R. Evolving Expressive Music Performance through Interaction of Artificial Agent Performers,In Proceedings of ECAL 2007 Workshop on Music and Artificial Life, 2007.
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Costalonga, L.L., Vicari, R.M. & Miletto, E.M. Agent-based guitar performance simulation. J Braz Comp Soc 14, 19–29 (2008). https://doi.org/10.1007/BF03192562
- music performance
- guitar computational model