Measuring the haptic behavior of an acoustic guitar as perceived by the player by means of a vibrating actuator
Bibliographic record
Abstract
Two sets of recordings of the vibration produced by plucking the fifth and the second string of an acoustic guitar were acquired using an accelerometer secured to the neck of the instrument. Vibrations from both sets could be reproduced using a recoil-type vibrating actuator attached at the neck of the guitar. In one of the sets, salient spectral features of the original recordings were altered. We performed a preliminary study involving nine volunteer participants, blindfolded and artificially deafened using earplugs and loud white noise played through headphones. They were asked to discriminate, by holding the neck of the instrument with their left hand, between “fake” (i.e., actuator-produced) or real vibrations, produced by the experimenter plucking either string two or five. Our aim was to assess if any of the spectral features altered in the second set of recordings increased the recognition rate of actuator-produced vibrations as being “fake.” These features, if present, would then be likely to carry crucial information and should be therefore modeled with extreme care in the simulation of the haptic behavior of the instrument. Results show that, at least for string five, we were capable of identifying one feature (a peak in the vibration spectrum located at 548 Hz), which, if altered, made the recognition rate as “fake” rise, statistically significantly, from 55% to 89%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".