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IDyOM (Information Dynamics of Music) is a system for constructing multiple-viewpoint, variable-order Markov models for predictive modelling of probabilistic structure in symbolic, sequential auditory domains such as music. IDyOM acquires knowledge about a domain through statistical learning and generates conditional probability distributions representing the estimated likelihood of each event in a sequence, plus associated information-theoretic measures, given the preceding context and prior short- and long-term training of the model.

This wiki provides documentation for IDyOM users and developers.

See also the tutorial: https://github.com/mtpearce/idyom-tutorial

User documentation

The IDyOM documentation assumes a basic knowledge of Common Lisp, and familiarity with the system's purpose and underlying theory. For more information, see Pearce (2005) (thesis.pdf in the distribution) and other related publications.

• Installing and loading IDyOM and its prerequisites
• Database management
• Multiple viewpoints
  • Working with viewpoints
  • List of viewpoints
• Running IDyOM
  • IDyOM Parameters
  • IDyOM Output
  • IDyOM Examples
  • Segmentation
• Troubleshooting

Developer documentation

This documentation is designed to help with the development of IDyOM.

• Code Architecture
• Development roadmap
• Specific modules:
  • Adding new viewpoints
  • The ppm-star package
  • The resampling package
  • The viewpoint-selection package