KNOWLEDGE-DRIVEN HARMONIZATION MODEL FOR TONAL MUSIC

Mariusz Rybnik, Władysław Homenda

2012

Abstract

The paper proposes an approach to an automatic harmonization of musical work, and is based on the knowledge of music theory. It may be described as knowledge-based, being in contrast to a data-driven approach, that extracts relationships from examples. Our approach emphasizes universality, understood as the possibility of direct model modifications in order to obtain varied harmony characteristics (as for example a complicated and unusual harmony, or a simple harmony using only a small subset of harmonic functions and few modifiers). Therefore it is configurable by changing the internal parameters of harmonization mechanisms (among others: harmonic functions excitements with note pitches, note importance regarding among others horizontal position in measure and vertical position in voices structure, successions of neighboring harmonic functions), as well as importance weights attached to each of these mechanisms.

References

  1. Cope, D. (1987). An expert system for computer-assisted music composition. In Computer Music Journal, volume 11 (4), pages 30-46.
  2. Ebcioglu, K. (1993). An expert system for harmonizing four-part chorales. In Machine Models of Music, pages 385-401. MIT Press.
  3. Hild, H. Feulner, J. M. W. (1992). Harmonet: A neural net for harmonizing chorals in the style of j.s. bach. In Advances in Neural Information Processing 4.
  4. Pachet, F. and Roy, P. (2001). Musical harmonization with constraints: A survey. In Constraints Journal, volume 6(1), pages 7-19. Kluwer Publisher.
  5. Pachet, F. Roy, P. (1995). Mixing constraints and objects: a case study in automatic harmonization. In TOOLS Europe 7895, pages 119-126. Prentice-Hall.
  6. Prisco, R. D. and Zaccagnino, R. (2009). An evolutionary music composer algorithm for bass harmonization. In Applications of Evolutionary Computing. LNCS.
  7. Sikorski, K. (2003). Harmony part 1 and 2 [in Polish]. Polskie Wydawnictwo Muzyczne.
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Paper Citation


in Harvard Style

Rybnik M. and Homenda W. (2012). KNOWLEDGE-DRIVEN HARMONIZATION MODEL FOR TONAL MUSIC . In Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-95-9, pages 445-450. DOI: 10.5220/0003889604450450


in Bibtex Style

@conference{icaart12,
author={Mariusz Rybnik and Władysław Homenda},
title={KNOWLEDGE-DRIVEN HARMONIZATION MODEL FOR TONAL MUSIC},
booktitle={Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2012},
pages={445-450},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003889604450450},
isbn={978-989-8425-95-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - KNOWLEDGE-DRIVEN HARMONIZATION MODEL FOR TONAL MUSIC
SN - 978-989-8425-95-9
AU - Rybnik M.
AU - Homenda W.
PY - 2012
SP - 445
EP - 450
DO - 10.5220/0003889604450450