Skip to main content

A Groovy Virtual Drumming Agent

  • Conference paper
Intelligent Virtual Agents (IVA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5773))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Tidemann, A., Demiris, Y.: Groovy neural networks. In: 18th European Conference on Artificial Intelligence, vol. 178, pp. 271–275. IOS press, Amsterdam (2008)

    Google Scholar 

  2. 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)

    Chapter  Google Scholar 

  3. Piaget, J.: Play, dreams and imitation in childhood. W. W. Norton, New York (1962)

    Google Scholar 

  4. Meltzoff, A.N., Moore, M.K.: Imitation of facial and manual gestures by human neonates. Science 198, 75–78 (1977)

    Article  Google Scholar 

  5. Rizzolatti, G., Fadiga, L., Gallese, V., Fogassi, L.: Premotor cortex and the recognition of motor actions. Cognitive Brain Research 3, 131–141 (1996)

    Article  Google Scholar 

  6. Schaal, S.: Is imitation learning the route to humanoid robots? Trends in Cognitive Sciences 3(6), 233–242 (1999)

    Article  Google Scholar 

  7. Arbib, M.: The Mirror System, Imitation, and the Evolution of Language. In: Imitation in animals and artifacts, pp. 229–280. MIT Press, Cambridge (2002)

    Google Scholar 

  8. Gallese, V., Goldman, A.: Mirror neurons and the simulation theory of mind-reading. Trends in Cognitive Sciences 2(12) (1998)

    Google Scholar 

  9. Jordan, M.I., Rumelhart, D.E.: Forward models: Supervised learning with a distal teacher. Cognitive Science 16, 307–354 (1992)

    Article  Google Scholar 

  10. Demiris, Y., Khadhouri, B.: Hierarchical attentive multiple models for execution and recognition of actions. Robotics and Autonomous Systems 54, 361–369 (2006)

    Article  Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. Wolpert, D.M., Miall, R.C., Kawato, M.: Internal models in the cerebellum. Trends in Cognitive Sciences 2(9) (1998)

    Google Scholar 

  13. 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)

    Chapter  Google Scholar 

  14. 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)

    Google Scholar 

  15. Pachet, F.: Enhancing Individual Creativity with Interactive Musical Reflective Systems. Psychology Press (2006)

    Google Scholar 

  16. de Mantaras, R.L., Arcos, J.L.: AI and music from composition to expressive performance. AI Mag 23(3), 43–57 (2002)

    Google Scholar 

  17. Weinberg, G., Driscoll, S.: Robot-human interaction with an anthropomorphic percussionist. In: CHI 2006 Proceedings, April 2006, pp. 1229–1232 (2006)

    Google Scholar 

  18. Haruno, M., Wolpert, D.M., Kawato, M.: MOSAIC model for sensorimotor learning and control. Neural Comp 13(10), 2201–2220 (2001)

    Article  MATH  Google Scholar 

  19. Jaeger, H., Haas, H.: Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication. Science 304(5667), 78–80 (2004)

    Article  Google Scholar 

  20. Gusfield, D.: Algorithms on strings, trees, and sequences: computer science and computational biology. Cambridge University Press, New York (1997)

    Book  MATH  Google Scholar 

  21. Kawato, M.: Feedback-error-learning neural network for supervised motor learning. In: Eckmiller, R. (ed.) Advanced neural computers, pp. 365–372 (1990)

    Google Scholar 

  22. Nehaniv, C.L., Dautenhahn, K.: The Correspondence Problem. In: Imitation in Animals and Artifacts, pp. 41–63. MIT Press, Cambridge (2002)

    Google Scholar 

  23. Torres, E.B., Zipser, D.: Simultaneous control of hand displacements and rotations in orientation-matching experiments. J. Appl. Physiol. 96(5), 1978–1987 (2004)

    Article  Google Scholar 

  24. Tolani, D., Badler, N.I.: Real-time inverse kinematics of the human arm. Presence 5(4), 393–401 (1996)

    Article  Google Scholar 

  25. Kandel, E.R., Schwartz, J.H., Jessell, T.M.: Principles of neural science. McGraw-Hill, New York (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints 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)

Publish with us

Policies and ethics