Adaptive modelling of digital hearing aids using a subband affine projection algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Adaptive modeling of digital hearing aids is useful in characterizing the hearing aid behavior in response to “real world” stimuli such as speech and music. Most modern hearing aids employ amplitude compression in different frequency bands for effective mapping of the wide dynamic range audio signals into the reduced dynamic range of the hearing impaired listeners. Due to the presence of independent compression channels, the conventional fullband adaptive model might not adequately characterize the performance of a multichannel compression hearing aid (MCHA). In this paper, we propose a subband adaptive modeling approach to characterize the electroacoustic performance of a MCHA. The proposed structure employs uniform, oversampled DFT filterbanks for analysis and synthesis, and the affine projection algorithm for adaptive modeling in each subband. Experiments with simulated MCHAs showed that the subband structure outperforms the fullband structure under a variety of operating conditions.
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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 | 0.000 | 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 | 0.001 |
| 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 it