Misophonia Sound Recognition Using Vision Transformer
Bibliographic record
Abstract
Misophonia is a condition characterized by an abnormal emotional response to specific sounds, such as eating, breathing, and clock ticking noises. Sound classification for misophonia is an important area of research since it can benefit in the development of interventions and therapies for individuals affected by the condition. In the area of sound classification, deep learning algorithms such as Convolutional Neural Networks (CNNs) have achieved a high accuracy performance and proved their ability in feature extraction and modeling. Recently, transformer models have surpassed CNNs as the dominant technology in the field of audio classification. In this paper, a transformer-based deep learning algorithm is proposed to automatically identify trigger sounds and the characterization of these sounds using acoustic features. The experimental results demonstrate that the proposed algorithm can classify trigger sounds with high accuracy and specificity. These findings provide a foundation for future research on the development of interventions and therapies for misophonia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".