INVENTING NEW INSTRUMENTS BASED ON A COMPUTATIONAL 'HACK' TO MAKE AN OUT-OF-TUNE OR UNPITCHED INSTRUMENT PLAY IN PERFECT HARMONY
Bibliographic record
Abstract
We begin with a case-study of one of our public art installations, a large waterflute, which is a member of a class of water-based instruments that we call "hydraulophones".Hydraulophones are like wind instruments but they use matter in its liquid state (water) in place of matter in the gaseous state (wind).The particular waterflute in question has the unique property that it has never been tuned, and, additionally, due to what would appear to be a theft from an underground vault just before the main public opening, a number of important parts went missing.Additionally, due to some errors in the installation, we had to use some creative and improvisational computation in order to make the instrument "sing" in perfect harmony.What we learned from this case-study, was a specific technique that allows computation to be used to make almost any out-of-tune, broken, or quickly built/improvised instrument play in perfect harmony, as long as a separate acoustic pickup can be used for each note.Our method uses a filterbank in which sound from each pickup is processed with a filter having a transfer function that maps the out-of-tune or otherwise "broken" sound to the desired sound at the desired pitch (optionally with acoustic feedback to excite the original acoustic process toward the proper pitch) without losing too much of the musical expressivity and physicality of the original acoustic instrument.We also propose the use of other techniques such as computer vision to relax the requirement of having separate pickups for each note, while maintaining the physicality of an acoustic instrument.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".