Texture Classification Based on Sound and Vibro-Tactile Data
Bibliographic record
Abstract
This paper focuses on the development and validation of an automatic learning system for the classification of tactile data in form of vibro-tactile (accelerometer) and audio (microphone) data for texture recognition. A novel combination of features including the standard deviation, the mean, the absolute median of the deviation, the energy that characterizes the power of the signal, a measure that reflects the perceptual properties of the human system associated with each sensory modality, and the Fourier characteristics extracted from signals, along with principal component analysis, is shown to obtain the best results. Several machine learning models are compared in an attempt to identify the best compromise between the number of features, the classification performance and the computation time. Longer sampling periods (2 s. vs. 1 s) provide more information for classification, leading to higher performance (average of 3.59%) but also augment the evaluation time by an average of 29.48% over all features and models. For the selected dataset, the XGBRF model was identified to represent overall the best compromise between performance and computation time for the proposed novel combination of features over all material types with an F-score of 0.91 and a computation time of 4.69 ms, while kNN represents the next best option (1% improvement in performance at the cost of 2.13 ms increase in time with respect to XGBRF).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".