Information Dynamics Of Music or IDyOM (Pearce, 2005): a framework for constructing multiple-viewpoint variable-order Markov models for predictive statistical modelling of musical structure (see Conklin, 1990, Conklin & Witten, 1995). The system generates a conditional probability distribution representing the estimated likelihood of each note in a melody, given the preceding musical context; it computes Shannon entropy as a measure of uncertainty about the next note and information content as a measure of the unexpectedness of the note that actually follows.

The IDyOM software is written in the programming language Common Lisp and is made available under the GNU General Public License.

Downloads and documentation:

  • For downloads, installation and usage, visit the Wiki.
  • Further scientific publications are available here.

To cite the software in your research, please reference:

Pearce, M. T. (2005). The Construction and Evaluation of Statistical Models of Melodic Structure in Music Perception and Composition. Doctoral Dissertation, Department of Computing, City University, London, UK.

I'd be interested to hear if you're using IDyOM in your research, so do drop me a note (). If you would like to contribute to the development of IDyOM, please get in touch and see also the Development Roadmap for things currently in progress or planned.

Related publications

M. Pearce, “The Construction and Evaluation of Statistical Models of Melodic Structure in Music Perception and Composition,” PhD thesis, School of Informatics, City University, London, 2005.
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M. Pearce, D. Müllensiefen, and G. Wiggins, “The role of expectation and probabilistic learning in auditory boundary perception: A model comparison,” Perception, vol. 39, no. 10, pp. 1365–1389, 2010.
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D. Conklin, “Prediction and Entropy of Music.” Department of Computer Science, University of Calgary, 1990.
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D. Omigie, M. Pearce, and L. Stewart, “Tracking of pitch probabilities in congenital amusia,” in Neuropsychologia, 2012, vol. 50, pp. 1483–1493.
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D. Omigie, M. Pearce, V. Williamson, and L. Stewart, “Electrophysiological correlates of melodic processing in congenital amusia,” in Neuropsychologia, 2013, vol. 51, pp. 1749–1762.
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M. Pearce, “Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation,” Annals of the New York Academy of Sciences, 2018.
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D. Conklin and I. H. Witten, “Multiple viewpoint systems for music prediction,” Journal of New Music Research, vol. 24, no. 1, pp. 51–73, 1995.
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N. C. Hansen and M. Pearce, “Predictive Uncertainty in Auditory Sequence Processing,” in Frontiers in Psychology, 2014, vol. 5, p. 1052.
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Manager: Marcus Pearce
Developer: Marcus Pearce, Peter Harrison