2018-06-13 status

Done

Methodology

• Contemplated a survey to find ground truth for interchangeable digits. (Discussed briefly with AFL and JB over email.)

Model Building

• Implemented trigram nodes in networkx for revamped Parncutt.

Doing

1. Learning Cytoscape for graph visualization to help debug graph code.
2. Fixing cost functions in Parncutt.
3. Validating Parncutt cost functions for left hand.
4. Adding mechanism to learn weights for Parncutt rules from training data.
5. Reading some fingering pedagogy (C. P. E. Bach, Couperin, Rami Bar-Niv) to develop a vocabulary for talking to pianists.

Struggling

1. A body at rest tends to stay at rest.
2. Does this topic make sense for my new career situation?

In Scope

1. Implementing crude automatic segmenter.
2. Developing staccato/legato classifier.
3. Demonstrating improved Parncutt via #1 and #2.
4. Debugging Sayegh model, which produces results inconsistent with training data.
5. Developing better test cases for Sayegh.
6. Updating abcDE to support manual segmentation.
7. Completing and polishing abcD for entire Beringer corpus.
8. Defining initial benchmark corpora and evaluation methodology.
9. Implementing convenience methods for reporting benchmark results.
10. Moving Beringer corpus to MySQL database.
11. Enhancing Parncutt, following published techniques and pushing beyond them.
12. Enhancing Hart and Sayegh to return top n solutions.
13. Re-weighting Parncutt rules using machine learning and TensorFlow. (This seems like a good fit.)
14. Adding support to abcDE for annotating phrase segmentation.
15. Debugging Dactylize 88-key circuit.
16. Collecting fingering data from JB performances in Elizabethtown.
17. Completing Dactylize II circuit.
18. 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).
19. Defining procedure for sanity test of production automatic data collector (including Beringer data).
20. Defining corpora for Dactylize data collection (WTC, Beringer, ??).
21. Implementing end-to-end machine learning experiment, using Beringer abcD data.
22. 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.