Fiber Materials and Musical Perception: Exploring Tactile Transmission of Sound
Bibliographic record
Abstract
Objective: This study aims to systematically evaluate the performance of functional textile materials as vibrotactile feedback media. By characterizing the vibration transmission properties of various fabrics, we provide a materials science basis for developing novel wearable devices for music perception among hearing-impaired individuals. Methods: The experiment utilized three representative engineering textiles: polyester, nylon, and carbon fiber woven fabrics. Piezoelectric sensing technology was employed to convert audio signals into mechanical vibrations. Accelerometers were used to precisely measure the vibration conduction efficiency of each fabric, while 240 participants were recruited to assess tactile recognition rates. The research focused on the influence of different fiber raw materials and fabric structures (e.g., plain, twill) on vibration transmission performance. Results: The results demonstrate that the carbon fiber plain weave fabric exhibited optimal vibration conduction efficiency (4.85 m/s² in the low-frequency range <250 Hz), significantly outperforming the nylon and polyester fabrics. Tactile feedback based on this high-performance textile yielded a recognition rate of 92% for low-frequency music. Furthermore, the rhythm perception accuracy of the hearing-impaired group improved to 85%, proving the distinct advantage of advanced textile materials in the high-fidelity transmission of low-frequency vibrations. Conclusion: This research validates that textile-based substrates can serve as effective media for tactile music transmission. By virtue of its superior mechanical vibration transmission, carbon fiber fabric provides critical material science data and design parameters for the development of “smart textiles” and wearable sensing devices tailored for specific populations. This work shows broad application prospects in the interdisciplinary field of “textile engineering” and assistive rehabilitation technology.
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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) | 0.001 | 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".