Hyperacoustic instruments: Computer-controlled instruments that are not electrophones
Bibliographic record
Abstract
This paper describes a musical instrument consisting of a physical process that acoustically generates sound from the material world (i.e. sound derived from matter such as solid, liquid, gas, or plasma) which is modified by a secondary input from the informatic world. This informatic input selects attributes such as the frequency range of the musical note being sounded, while the acoustic process is kept in close contact with the user, to ensure a high degree of expressivity. In one example, ice skates with acoustic pickups are used to play music while the skater simultaneously controls a bandpass filter with a hand-held keyer and wearable computer. Each skate works much like the bow on a violin, allowing the player to hit, scrape, rub, or "bow", the ice in various ways to create a wide variety of musical textures. Additionally the player can select sound samples on a per-note basis and then "scratch" out a melody or harmony (playing multiple samples at once) on the ice on the rink like a team of disk jockeys (DJs) working together to "scratch" an array of vinyl records. Because the grooves on an ice rink are made by the player in a freeform fashion, there is much more room for variations in musical timbres and textures than with the fixed grooves of a record. Rather than merely using the keyer to trigger musical notes through MIDI note on/note off commands, we create acoustic sound through a physical process such as skating, and then turn those physical sounds into musical notes with the handheld keyer that functions as a modifier input. This combination combines the expressivity of non-electrophonic musical instruments like the violin with the flexibility of electrophones like the sound synthesizer. As a further contribution of the paper, a general taxonomy of acoustic transducers and a link to physical organology is provided, in which the top-level of the taxonomy is the state-of-matter in which the transducer operates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.019 |
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".