Adaptive environment classification system for hearing aids
Why this work is in the frame
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Bibliographic record
Abstract
An adaptive sound classification framework is proposed for hearing aid applications. The long-term goal is to develop fully trainable instruments in which both the acoustical environments encountered in daily life and the hearing aid settings preferred by the user in each environmental class could be learned. Two adaptive classifiers are described, one based on minimum distance clustering and one on Bayesian classification. Through unsupervised learning, the adaptive systems allow classes to split or merge based on changes in the ongoing acoustical environments. Performance was evaluated using real-world sounds from a wide range of acoustical environments. The systems were first initialized using two classes, speech and noise, followed by a testing period when a third class, music, was introduced. Both systems were successful in detecting the presence of an additional class and estimating its underlying parameters, reaching a testing accuracy close to the target rates obtained from best-case scenarios derived from non-adaptive supervised versions of the classifiers (about 3% lower performance). The adaptive Bayesian classifier resulted in a 4% higher overall accuracy upon splitting adaptation than the minimum distance classifier. Merging accuracy was found to be the same in the two systems and within 1%-2% of the best-case supervised versions.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 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 it