Facial expression analysis and auditory perception
Bibliographic record
Abstract
The psychoacoustic assessment of loudness disorders like Hyperacusis relies on subjective reports, which are biased and impractical for non-verbal patients. Similar issues in pain evaluation have been addressed using facial expression analysis to decode the intensity and affective value of perceived pain. Thus, we are developing a system for the objective evaluation of perceived sounds from a listener’s facial expression. We determine whether sensory (intensity) and affective (valence) dimensions of sound perception can be distinguished through facial expressions by employing action unit analysis and other facial feature extraction methods on our in-house dataset. This video dataset includes facial expressions in response to sounds with varying intensities and emotional valences. We train convolutional networks to decode these dimensions from both extracted features, such as AUs and raw video data. By employing feature and decision fusion methods, we are developing an automated system for the objective assessment of perceived sound. This system will pioneer the first objective method for assessing loudness disorders, enhancing patient care and potentially distinguishing between hyperacusis sub-types to facilitate personalized treatment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".