Roadmap » History » Version 27

Marcus Pearce, 2014-03-28 01:19 PM

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h1. Roadmap
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h2. Installation
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* -Remove absolute pathname in connect-to-database. (mtp-admin/music-data.lisp)-
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* Create cache directories if they don't exist.
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h2. Data import
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* -It is possible to import empty datasets, which cause an error when described.-
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h2. Viewpoints
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* Zero barlengths sometimes cause divide by zero errors
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* A straightforward language for specifying viewpoints, including viewpoint schemas (e.g. interval, interval size), making system more data agnostic
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* Polyphonic viewpoints
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h2. Viewpoint selection
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* Memory errors sometimes occur with large viewpoints sets (e.g., Cpitch with no basis specified)
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* When decimal places are restricted for comparison, earlier systems are preferred within a round, so use full precision to choose between ties
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* Print trace information about VP sets being tested + mean IC values; record this to log file.
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* Optionally specify: min-links
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* More flexible way for user to specify constraints on viewpoint search:
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** Define labelled viewpoint classes
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** Pairs/triples of labels/wildcards specify acceptable combinations
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** User provides whitelist or blacklist spec
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* Provide recommended viewpoints for more than just cpitch, onset & bioi
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h2. Testing
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* Include unit testing code.
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h1. Longer-term goals
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* 'Pace' viewpoint: measure of information rate (bits/sec), analogous to flow in speech production.
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* A web service.
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* Compute predictive information (PI), expected PI and PI rate (as analogs to IC, entropy and entropy rate respectively).
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* Predict over more than one dataset.
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* Hierarchical structure: chunk common patterns into symbols.
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Allow user to specify structure of model.
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* Determine order in which distributions are combined.
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* Specify weights for particular combinations, e.g. weighted viewpoints, or weighted memory stores.
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* Multiple memory stores.
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* Specify alternative context strategies (e.g., future context)
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* Provide some prepackaged models, e.g. the current model structure.
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Efficiency:
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** Check/extend caching of models etc.
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** Use sampling to estimate mean IC during VP selection.
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** Optimise viewpoint selection based on match with existing IC values.