2018-03-22 status

Done

Administrivia

• Requested suspension of TAA support until next January (for tax purposes).
• Answered interview questions for College of Engineering story on Fifty for the Future award.

Model Building

• Completed Sayegh implementation.
• Learned how to modify TatSu AST. 
• Implemented "pivot alignment" evaluation method.
• Refactored code for better reuse in new models, especially for segmenting input.
• Fixed how first and last fingering were being constrained to ensure model preferences for second and penultimate fingerings were not ignored.
• Drafted ISMIR abstract.
• Drew a diagram of the subproblems in the domain.

Doing

1. Writing up what we have done so far.
2. Reimplementing Parncutt model in framework using networkx.
3. Developing better test cases for Sayegh.
4. Implementing crude automatic segmenter.
5. Updating abcDE to support manual segmentation.
6. Completing and polishing abcD for entire Beringer corpus.
7. Defining initial benchmark corpora and evaluation methodology.
8. Implementing convenience methods for reporting benchmark results.

Struggling

1. Sayegh model produces results that do not seem consistent with training data provided.

In Scope

1. Moving Beringer corpus to MySQL database.
2. Enhancing Parncutt, following published techniques and pushing beyond them.
3. Enhancing Hart and Sayegh to return top n solutions.
4. Re-weighting Parncutt rules using machine learning and TensorFlow. (This seems like a good fit.)
5. Adding support to abcDE for annotating phrase segmentation.
6. Debugging Dactylize 88-key circuit.
7. Collecting fingering data from JB performances in Elizabethtown.
8. Completing Dactylize II circuit.
9. Developing method to align performance data with symbolic data. I think this is going to be essential if we are to use Dactylize data moving forward and a key part of its proof of concept. I plan to have something for this at the ISMIR demo session (September 22 deadline).
10. Defining procedure for sanity test of production automatic data collector (including Beringer data).
11. Defining corpora for Dactylize data collection (WTC, Beringer, ??).
12. Implementing end-to-end machine learning experiment, using Beringer abcD data.
13. Submitting papers to TISMIR. Ideas: a follow-up demo paper describing Dactylize data collected; a full-length paper describing application of evaluation method to models developed; a full-length description of enhanced and/or novel models, demo of method to align collected performance data with symbolic score.