Roadmap » History » Version 15
Version 14 (Jeremy Gow, 2012-11-22 10:55 PM) → Version 15/34 (Jeremy Gow, 2012-12-02 10:29 AM)
h1. Roadmap
The development code has been made compatible with sbcl 1.1.
The immediate goal is to release a version that works with the built-in
examples (Conklin 95 etc.). Need to fix problems created by datasets with
incomplete viewpoints.
h2. Short-term
New basic viewpoints:
* -cents - a higher resolution representation of pitch. [High priority for Makam data.]-
Viewpoint selection:
* Adding viewpoint weights to output.
* Print trace information about VP sets being tested + mean IC values; record this to log file.
* Optionally specify: start point for search, min-links
* More flexible way for user to specify constraints on viewpoint search:
** Define labelled viewpoint classes
** Pairs/triples of labels/wildcards specify acceptable combinations
** User provides whitelist or blacklist spec
Efficiency:
* Check/extend caching of models etc.
* Use sampling to estimate mean IC during VP selection.
Some basic benchmarks to ensure stability of future development versions.
h2. Mid-term goals
New viewpoint: metrical contour.
Make system more data agnostic:
* Remove dependancy on amuse.
* Separation of music viewpoints from model.
* A straightforward interface for specifying viewpoints, including viewpoint schemas (e.g. interval, interval size)
Viewpoint selection:
* Optimise based on match with existing IC values.
Allow user to specify structure of model.
* Determine order in which distributions are combined.
* Specify weights for particular combinations, e.g. weighted viewpoints, or weighted memory stores.
* Multiple memory stores.
* Specify alternative context strategies.
* Provide some prepackaged models, e.g. the current model structure.
Allow models to use predictive information (PI), expected PI and PI rate (as analogs to IC, entropy and entropy rate respectively).
Predict more than one basic viewpoint, and provide recommended viewpoints for each one (not just cpitch and bioi).
Predict over more than one dataset. viewpoint
h2. Long-term
Hierarchical structure: chunk common patterns into symbols.
Parallel implementation.
h2. Minor problems to fix
* It is possible to import empty datasets, which cause an error when described.
* Create root directory structure if not present.
* Show all viewpoints in describe-dataset.
* Zero barlength (e.g. in Makam data) causes divide by zero error.
* Update kern import to handle new pitch viewpoints.
The development code has been made compatible with sbcl 1.1.
The immediate goal is to release a version that works with the built-in
examples (Conklin 95 etc.). Need to fix problems created by datasets with
incomplete viewpoints.
h2. Short-term
New basic viewpoints:
* -cents - a higher resolution representation of pitch. [High priority for Makam data.]-
Viewpoint selection:
* Adding viewpoint weights to output.
* Print trace information about VP sets being tested + mean IC values; record this to log file.
* Optionally specify: start point for search, min-links
* More flexible way for user to specify constraints on viewpoint search:
** Define labelled viewpoint classes
** Pairs/triples of labels/wildcards specify acceptable combinations
** User provides whitelist or blacklist spec
Efficiency:
* Check/extend caching of models etc.
* Use sampling to estimate mean IC during VP selection.
Some basic benchmarks to ensure stability of future development versions.
h2. Mid-term goals
New viewpoint: metrical contour.
Make system more data agnostic:
* Remove dependancy on amuse.
* Separation of music viewpoints from model.
* A straightforward interface for specifying viewpoints, including viewpoint schemas (e.g. interval, interval size)
Viewpoint selection:
* Optimise based on match with existing IC values.
Allow user to specify structure of model.
* Determine order in which distributions are combined.
* Specify weights for particular combinations, e.g. weighted viewpoints, or weighted memory stores.
* Multiple memory stores.
* Specify alternative context strategies.
* Provide some prepackaged models, e.g. the current model structure.
Allow models to use predictive information (PI), expected PI and PI rate (as analogs to IC, entropy and entropy rate respectively).
Predict more than one basic viewpoint, and provide recommended viewpoints for each one (not just cpitch and bioi).
Predict over more than one dataset. viewpoint
h2. Long-term
Hierarchical structure: chunk common patterns into symbols.
Parallel implementation.
h2. Minor problems to fix
* It is possible to import empty datasets, which cause an error when described.
* Create root directory structure if not present.
* Show all viewpoints in describe-dataset.
* Zero barlength (e.g. in Makam data) causes divide by zero error.
* Update kern import to handle new pitch viewpoints.