Bibliographic record
Abstract
We introduce a new musical instrument in which computation is used to modify acoustically generated sounds. The acoustically generated sounds originate from real physical objects in the user's environment. These sounds are picked up by one or more microphones connected to a camera phone which filters the sounds using filters whose coefficients change in response to subject matter present in view of the camera. In one example, a row of 12 image processing zones is presented such that sounds originating from real world objects in the first zone are mapped to the first note on a musical scale, sounds originating from the second zone are mapped to the second note of the musical scale, and so on. Thus a user can hit a cement wall or sidewalk, or the ground, and the camera phone will transform the resulting sound (e.g. a dull "thud") into a desired sound, such as the sound of tubular bells, chimes, or the like. Note that the instrument is not an electronic instrument (i.e. not an Electrophone in the Hornbostel Sachs sense) because the sound originates acoustically and is merely filtered toward the desired note. This plays upon the acoustic qualities and physicality of the originating media. For example, if we strike the ground abruptly, the sound resembles that of a bell being hit abruptly. If we rub the ground, the sound resembles that of rubbing a bell. We can scrape the ground in various ways to obtain various sounds that differ depending on which of the camera's zones we're in, as well as the physical properties of the ground itself. These experiences can be shared across "cyborgspace" to effectively blur the boundary between the real and virtual worlds. We present an aquatic instrument that plays upon jets of water, where it is the filter coefficients of the transform that are shared. This allows both users to play the instrument in the jets of water of different public fountains but still experience the same musical qualities of the instrument, and share the physical experience of playing in a fountain despite geographic distances.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".