Auditory and vibrotactile interactions in perception of timbre acoustic features
Bibliographic record
Abstract
Recently, there has been increasing interest in developing auditory-to-vibrotactile sensory devices. However, the potential of these technologies is constrained by our limited understanding of which features of complex sounds can be perceived through vibrations. The present study aimed to investigate the vibrotactile perception of acoustic features related to timbre, an essential component to identify environmental, speech and musical sounds. Discrimination thresholds were measured for six features: three spectral (number of harmonics, harmonic roll-off ratio, even-harmonic attenuation) and three temporal (attack time, amplitude modulation depth and amplitude modulation frequency) using auditory, vibrotactile and combined auditory + vibrotactile stimulation in 31 adult humans with normal tactile and auditory sensitivity. Result revealed that all spectral and temporal features can be reliably discriminated via vibrotactile stimulation only. However, for spectral features, vibrotactile thresholds were significantly higher (i.e., worse) than auditory thresholds whereas, for temporal features, only vibrotactile amplitude modulation frequency was significantly higher. With simultaneous auditory and tactile presentation, thresholds significantly improved for attack time and amplitude modulation depth, but not for any of the spectral acoustic features. These results suggest that vibrotactile temporal cues have a more straightforward potential for assisting auditory perception, while vibrotactile spectral cues may require specialized signal processing schemes.
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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) | 0.000 | 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 | 0.000 |
| 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".