Perception of sound renderings via vibrotactile feedback
Bibliographic record
Abstract
The vibration behavior of acoustic music instruments, as perceived through touch, is known to play an important role in the interaction between performers and music instruments. This research explores the relevance and utility of including a tactile simulation of this behavior in computer music interfaces, in a snapshot taken during early learning, and including tradeoffs of using this channel for other information. Vibrotactile (VT) renderings of different types of sounds were presented to subjects via a handle fitted with vibration actuators. Subjects (1) evaluated consistency of the sound with the VT rendering, (2) identified from a “lineup” the VT rendering that did not match a sound, and (3) provided data on how inclusion of VT cues in a rhythm-tapping task affected perception quality of the VT rendering. For the last, a distractor task allowed us to measure both the usability of the cues and the degradation in perception of the VT rendering. All renderings were either played directly through vibration transducers, or first altered by modifying the sound's frequency content. VT playback was delivered at intensities similar to those experienced in traditional acoustic music instruments. Subjects indicated that specific alterations to the original, direct VT rendering were consistent with the source sound more often than the original was, and were able to differentiate between correct and incorrect renderings of a source sound. However, the latter ability was masked when subjects were provided with and able to effectively utilize extra VT cues added to the VT feedback to improve their performance, suggesting that the VT cues were of greater utility than the VT mimicking. We discuss the relevance of these findings on the design of computer music interfaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".