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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.
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
- Working with viewpoints
- Running IDyOM
- Troubleshooting
This documentation is designed to help with the development of IDyOM.
- Code Architecture
- Development roadmap
- Specific modules:
- Viewpoints: Adding new viewpoints
- Interface to the ppm-star package
- The resampling package
- The viewpoint-selection package
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User documentation
- Installation
- Database management
- Multiple Viewpoints
- Running IDyOM
- Troubleshooting
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Developer documentation