A Novelty Search and Power-Law-Based Genetic Algorithm for Exploring Harmonic Spaces in J.S. Bach Chorales

  • Bill Manaris
  • David Johnson
  • Yiorgos Vassilandonakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8601)


We present a novel, real-time system, called Harmonic Navigator, for exploring the harmonic space in J.S. Bach Chorales. This corpus-based environment explores trajectories through harmonic space. It supports visual exploration and navigation of harmonic transition probabilities through interactive gesture control. These probabilities are computed from musical corpora (in MIDI format). Herein we utilize the 371 J.S. Bach Chorales of the Riemenschneider edition. Our system utilizes a hybrid novelty search approach combined with power-law metrics for evaluating fitness of individuals, as a search termination criterion. We explore how novelty search can aid in the discovery of new harmonic progressions through this space as represented by a Markov model capturing probabilities of transitions between harmonies. Our results demonstrate that the 371 Bach Chorale harmonic space is rich with novel aesthetic possibilities, possibilities that the grand master himself never realized.


Novelty Search Genetic Algorithm Markov Model Harmony Generative Music 


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  1. 1.
    Cope, D.: Virtual Music: Computer Synthesis of Musical Style. MIT Press, Cambridge (2004)Google Scholar
  2. 2.
    Eigenfeldt, A., Pasquier, P.: Realtime Generation of Harmonic Progressions Using Controlled Markov Selection. In: 1st International Conference on Computational Creativity (ICCC-X), pp. 16–25. ACM Press, New York (2010)Google Scholar
  3. 3.
    Legname, O.: Density Degree of Intervals and Chords. 20th Century Music 4(11), 8–14 (1997)Google Scholar
  4. 4.
    Lehman, J., Stanley, K.: Abandoning Objectives: Evolution through the Search for Novelty Alone. Evolutionary Computation Journal 19(2), 189–223 (2011)CrossRefGoogle Scholar
  5. 5.
    Manaris, B., Romero, J., Machado, P., Krehbiel, D., Hirzel, T., Pharr, W., Davis, R.B.: Zipf’s Law, Music Classification and Aesthetics. Computer Music Journal 29(1), 55–69 (2005)CrossRefGoogle Scholar
  6. 6.
    Manaris, B., Roos, P., Machado, P., Krehbiel, D., Pellicoro, L., Romero, J.: A Corpus-Based Hybrid Approach to Music Analysis and Composition. In: 22nd Conference on Artificial Intelligence (AAAI 2007), pp. 839–845. AAAI Press, Palo Alto (2007)Google Scholar
  7. 7.
    Manaris, B., Hughes, D., Vassilandonakis, Y.: Monterey Mirror: Combining Markov Models, Genetic Algorithms, and Power Laws. In: 1st Workshop in Evolutionary Music, 2011 IEEE Congress on Evolutionary Computation (CEC 2011), pp. 33–40. IEEE Press, Piscataway (2011)Google Scholar
  8. 8.
    Manaris, B., Johnson, D., Vassilandonakis, Y.: Harmonic Navigator: A Gesture-Driven, Corpus-Based Approach to Music Analysis, Composition, and Performance. In: 2nd International Workshop on Musical Metacreation (MUME 2013), 9th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 67–74. AAAI Press, Palo Alto (2013)Google Scholar
  9. 9.
    Johnson, D., Manaris, B., Vassilandonakis, Y.: Harmonic Navigator: An Innovative, Gesture-Driven User Interface for Exploring Harmonic Spaces in Musical Corpora. In: Kurosu, M. (ed.) HCI 2014, Part II. LNCS, vol. 8511, pp. 58–68. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  10. 10.
    Manaris, B., Roos, P., Krehbiel, D., Zalonis, T., Armstrong, J.R.: Zipf’s Law, Power Laws and Music Aesthetics. In: Li, T., Ogihara, M., Tzanetakis, G. (eds.) Music Data Mining, pp. 169–216. CRC Press, Boca Raton (2011)CrossRefGoogle Scholar
  11. 11.
    Pachet, F.: Beyond the Cybernetic Jam Fantasy: The Continuator. IEEE Computer Graphics and Applications 24(1), 31–35 (2004)CrossRefGoogle Scholar
  12. 12.
    Rosen, C.: The Classical Style; Haydn, Mozart, Beethoven, p. 26. W.W. Norton & Co., New York (1971)Google Scholar
  13. 13.
    Schoenberg, A.: Structural Functions of Harmony, pp. 192–196. W.W. Norton & Co., New York (1954)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Bill Manaris
    • 1
  • David Johnson
    • 1
  • Yiorgos Vassilandonakis
    • 2
  1. 1.Computer Science DepartmentCollege of CharlestonCharlestonUSA
  2. 2.Music DepartmentCollege of CharlestonCharlestonUSA

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