Skip to main content
SpringerLink
Log in

Using Autonomous Agents to Improvise Music Compositions in Real-Time

  • Conference paper
  • First Online:
Computational Intelligence in Music, Sound, Art and Design (EvoMUSART 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10198))

Abstract

This paper outlines an approach to real-time music generation using melody and harmony focused agents in a process inspired by jazz improvisation. A harmony agent employs a Long Short-Term Memory (LSTM) artificial neural network trained on the chord progressions of 2986 jazz ‘standard’ compositions using a network structure novel to chord sequence analysis. The melody agent uses a rule-based system of manipulating provided, pre-composed melodies to improvise new themes and variations. The agents take turns in leading the direction of the composition based on a rating system that rewards harmonic consistency and melodic flow. In developing the multi-agent system it was found that implementing embedded spaces in the LSTM encoding process resulted in significant improvements to chord sequence learning.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
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

References

  1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). http://tensorflow.org/, software available from tensorflow.org

  2. Barrett, F.J.: Coda–creativity and improvisation in jazz and organizations: implications for organizational learning. Organ. Sci. 9(5), 605–622 (1998)

    Article  Google Scholar 

  3. Bastien, D.T., Hostager, T.J.: Jazz as a process of organizational innovation. Commun. Res. 15(5), 582–602 (1988)

    Article  Google Scholar 

  4. Berliner, P.: Thinking in jazz: composing in the moment. Jazz Educ. J. 26, 241 (1994)

    Google Scholar 

  5. Biles, J.A.: Genjam in transition: from genetic jammer to generative jammer. In: Generative Art, vol. 2002 (2002)

    Google Scholar 

  6. Choi, K., Fazekas, G., Sandler, M.: Text-based LSTM networks for automatic music composition. arXiv preprint arXiv:1604.05358 (2016)

  7. Eck, D., Schmidhuber, J.: A first look at music composition using LSTM recurrent neural networks. Istituto Dalle Molle Di Studi Sull Intelligenza Artificiale 103 (2002)

    Google Scholar 

  8. Eigenfeldt, A., Pasquier, P.: A realtime generative music system using autonomous melody, harmony, and rhythm agents. In: XIII Internationale Conference on Generative Arts, Milan, Italy (2009)

    Google Scholar 

  9. Eigenfeldt, A., Pasquier, P.: Realtime generation of harmonic progressions using controlled Markov selection. In: Proceedings of ICCC-X-Computational Creativity Conference, pp. 16–25 (2010)

    Google Scholar 

  10. Folkestad, G., Hargreaves, D.J., Lindström, B.: Compositional strategies in computer-based music-making. Br. J. Music Educ. 15(01), 83–97 (1998)

    Article  Google Scholar 

  11. Gers, F.A., Schraudolph, N.N., Schmidhuber, J.: Learning precise timing with LSTM recurrent networks. J. Mach. Learn. Res. 3, 115–143 (2002)

    MathSciNet  MATH  Google Scholar 

  12. Johnson-Laird, P.N.: How jazz musicians improvise. Music Percept. Interdisc. J. 19(3), 415–442 (2002)

    Article  Google Scholar 

  13. Keller, R.M., Morrison, D.R.: A grammatical approach to automatic improvisation. In: Proceedings, Fourth Sound and Music Conference, Lefkada, Greece, July. Most of the soloists at Birdland had to wait for Parker’s next record in order to find out what to play next. What will they do now (2007)

    Google Scholar 

  14. Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  15. Levine, M.: The Jazz Theory Book. O’Reilly Media Inc., Sebastopol (2011)

    Google Scholar 

  16. Monson, I.: Jazz as political and musical practice. In: Musical Improvisation: Art, Education, and Society, pp. 21–37 (2009)

    Google Scholar 

  17. Pachet, F.: Enhancing individual creativity with interactive musical reflexive systems. In: Musical Creativity, pp. 359–375 (2006)

    Google Scholar 

  18. Pachet, F., Roy, P.: Imitative leadsheet generation with user constraints. In: ECAI, pp. 1077–1078 (2014)

    Google Scholar 

  19. Papadopoulos, A., Roy, P., Pachet, F.: Assisted lead sheet composition using FlowComposer. In: Rueher, M. (ed.) CP 2016. LNCS, vol. 9892, pp. 769–785. Springer, Cham (2016). doi:10.1007/978-3-319-44953-1_48

    Chapter  Google Scholar 

  20. Plans, D., Morelli, D.: Experience-driven procedural music generation for games. IEEE Trans. Comput. Intell. AI Games 4(3), 192–198 (2012)

    Article  Google Scholar 

  21. Rendel, A., Fernandez, R., Hoory, R., Ramabhadran, B.: Using continuous lexical embeddings to improve symbolic-prosody prediction in a text-to-speech front-end. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5655–5659. IEEE (2016)

    Google Scholar 

  22. Sturm, B.L., Santos, J.F., Ben-Tal, O., Korshunova, I.: Music transcription modelling and composition using deep learning. arXiv preprint arXiv:1604.08723 (2016)

Download references

Author information

Authors and Affiliations

  1. sensiLab, Faculty of Information Technology, Monash University, Caulfield East, Australia

    Patrick Hutchings & Jon McCormack

Authors
  1. Patrick Hutchings
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jon McCormack
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Patrick Hutchings .

Editor information

Editors and Affiliations

  1. University of Coimbra , Coimbra, Portugal

    João Correia

  2. RMIT University , Melbourne, Victoria, Australia

    Vic Ciesielski

  3. University of Malta , Msida, Malta

    Antonios Liapis

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Hutchings, P., McCormack, J. (2017). Using Autonomous Agents to Improvise Music Compositions in Real-Time. In: Correia, J., Ciesielski, V., Liapis, A. (eds) Computational Intelligence in Music, Sound, Art and Design. EvoMUSART 2017. Lecture Notes in Computer Science(), vol 10198. Springer, Cham. https://doi.org/10.1007/978-3-319-55750-2_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-55750-2_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55749-6

  • Online ISBN: 978-3-319-55750-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation