Translating Music to Touch: Exploring Tactile Perception of Pitch, Roughness, and Pleasantness
Bibliographic record
Abstract
Music is a rich multisensory experience, yet individuals with hearing impairments often lack access to this important aspect of culture. As tactile technologies advance, there is growing interest in whether musical information can be conveyed through vibration. This study investigates how core dimensions of auditory music perception, pitch, roughness, and pleasantness, can be translated into the tactile domain. Participants were asked to rate these perceptual dimensions for sinusoidal stimuli with frequencies ranging from 40 to 750 Hz, and for complex waveforms with fundamental frequencies of 52, 73, and 98 Hz, including amplitude modulated, sawtooth, and missing fundamental stimuli. Perceived pitch showed a systematic relationship with stimulus frequency for most participants, suggesting that tactile devices could at least partially convey some form of simple melodic patterns. The sawtooth waveform emerged as particularly effective for representing pitch changes, underscoring the role of rapid temporal transitions in tactile pitch encoding. Roughness ratings were negatively correlated with pleasantness, mirroring well-established findings in auditory perception. Waveforms with sudden temporal changes or rapid amplitude modulations were consistently judged as less pleasant. Taken together, these findings in normal hearing participants may inform the design of vibrotactile displays that could support access to selected music relevant perceptual dimensions, although generalization to people with hearing loss remains to be tested. Importantly, our results highlight the crucial role of the fast temporal envelope rather than the temporal fine structure, in shaping vibrotactile perception. Consistent with this interpretation, our data provide evidence for a tactile analog of the auditory missing fundamental phenomenon.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".