Abstract
This paper presents an architecture for an intelligent virtual agent that imitates human drumming behaviour. Through imitation, the agent models the user-specific variations that constitute the “groove” of the drummer. The architecture comprises a motor system that imitates arm movements of a human drummer, and a sound system that produces the sound of the human playing style. The presence of a sound system alleviates the need to use physical models that will create sound when a drum is struck, instead focusing on creating an imitative agent that booth looks and sounds similar to its teacher. Such a virtual agent can be used in a musical setting, where its visualization and sound system would allow it to be regarded as an artificial musician. The architecture is implemented using Echo State Networks, and relies on self-organization and a bottom-up approach when learning human drum patterns.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tidemann, A., Demiris, Y.: Groovy neural networks. In: 18th European Conference on Artificial Intelligence, vol. 178, pp. 271–275. IOS press, Amsterdam (2008)
Tidemann, A., Öztürk, P.: Self-organizing multiple models for imitation: Teaching a robot to dance the YMCA. In: Okuno, H.G., Ali, M. (eds.) IEA/AIE 2007. LNCS (LNAI), vol. 4570, pp. 291–302. Springer, Heidelberg (2007)
Piaget, J.: Play, dreams and imitation in childhood. W. W. Norton, New York (1962)
Meltzoff, A.N., Moore, M.K.: Imitation of facial and manual gestures by human neonates. Science 198, 75–78 (1977)
Rizzolatti, G., Fadiga, L., Gallese, V., Fogassi, L.: Premotor cortex and the recognition of motor actions. Cognitive Brain Research 3, 131–141 (1996)
Schaal, S.: Is imitation learning the route to humanoid robots? Trends in Cognitive Sciences 3(6), 233–242 (1999)
Arbib, M.: The Mirror System, Imitation, and the Evolution of Language. In: Imitation in animals and artifacts, pp. 229–280. MIT Press, Cambridge (2002)
Gallese, V., Goldman, A.: Mirror neurons and the simulation theory of mind-reading. Trends in Cognitive Sciences 2(12) (1998)
Jordan, M.I., Rumelhart, D.E.: Forward models: Supervised learning with a distal teacher. Cognitive Science 16, 307–354 (1992)
Demiris, Y., Khadhouri, B.: Hierarchical attentive multiple models for execution and recognition of actions. Robotics and Autonomous Systems 54, 361–369 (2006)
Wolpert, D.M., Doya, K., Kawato, M.: A unifying computational framework for motor control and social interaction. Philosophical Transactions: Biological Sciences 358(1431), 593–602 (2003)
Wolpert, D.M., Miall, R.C., Kawato, M.: Internal models in the cerebellum. Trends in Cognitive Sciences 2(9) (1998)
Saunders, C., Hardoon, D.R., Shawe-Taylor, J., Widmer, G.: Using string kernels to identify famous performers from their playing style. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 384–395. Springer, Heidelberg (2004)
Tobudic, A., Widmer, G.: Learning to play like the great pianists. In: Kaelbling, L.P., Saffiotti, A. (eds.) IJCAI, Professional Book Center, pp. 871–876 (2005)
Pachet, F.: Enhancing Individual Creativity with Interactive Musical Reflective Systems. Psychology Press (2006)
de Mantaras, R.L., Arcos, J.L.: AI and music from composition to expressive performance. AI Mag 23(3), 43–57 (2002)
Weinberg, G., Driscoll, S.: Robot-human interaction with an anthropomorphic percussionist. In: CHI 2006 Proceedings, April 2006, pp. 1229–1232 (2006)
Haruno, M., Wolpert, D.M., Kawato, M.: MOSAIC model for sensorimotor learning and control. Neural Comp 13(10), 2201–2220 (2001)
Jaeger, H., Haas, H.: Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication. Science 304(5667), 78–80 (2004)
Gusfield, D.: Algorithms on strings, trees, and sequences: computer science and computational biology. Cambridge University Press, New York (1997)
Kawato, M.: Feedback-error-learning neural network for supervised motor learning. In: Eckmiller, R. (ed.) Advanced neural computers, pp. 365–372 (1990)
Nehaniv, C.L., Dautenhahn, K.: The Correspondence Problem. In: Imitation in Animals and Artifacts, pp. 41–63. MIT Press, Cambridge (2002)
Torres, E.B., Zipser, D.: Simultaneous control of hand displacements and rotations in orientation-matching experiments. J. Appl. Physiol. 96(5), 1978–1987 (2004)
Tolani, D., Badler, N.I.: Real-time inverse kinematics of the human arm. Presence 5(4), 393–401 (1996)
Kandel, E.R., Schwartz, J.H., Jessell, T.M.: Principles of neural science. McGraw-Hill, New York (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tidemann, A., Öztürk, P., Demiris, Y. (2009). A Groovy Virtual Drumming Agent. In: Ruttkay, Z., Kipp, M., Nijholt, A., Vilhjálmsson, H.H. (eds) Intelligent Virtual Agents. IVA 2009. Lecture Notes in Computer Science(), vol 5773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04380-2_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-04380-2_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04379-6
Online ISBN: 978-3-642-04380-2
eBook Packages: Computer ScienceComputer Science (R0)