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Quantifying the generalization capacity of Markov models for melody prediction

Author

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  • Corrêa, Débora C.
  • Jüngling, Thomas
  • Small, Michael

Abstract

We analyze melodies of classical music by stochastic modeling and prediction, analogous to symbolic time series from a nonlinear dynamical system. The performance in a one-step prediction task indicates the capabilities of the models, given by Markov chains of different orders, to preserve prominent patterns of the compositions. We use cross-prediction between songs within a style, and between songs of different styles, to quantify how well the models can capture similarities between underlying dynamical rules. With this framework, the complexity and individuality of dynamical processes generating classical melodies can be systematically addressed.

Suggested Citation

  • Corrêa, Débora C. & Jüngling, Thomas & Small, Michael, 2020. "Quantifying the generalization capacity of Markov models for melody prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    • Handle: RePEc:eee:phsmap:v:549:y:2020:i:c:s0378437120301205
    DOI: 10.1016/j.physa.2020.124351
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    Cited by:

    1. Gündüz, Güngör, 2023. "Entropy, energy, and instability in music," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).

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