Beamforming Designs Robust to Propagation Model Estimation Errors for Binaural Hearing Aids
Bibliographic record
Abstract
This work introduces new binaural beamforming algorithms for hearing aids, with a good robustness to errors in the estimated target source propagation model. Two different binaural beamforming designs are proposed. One design is based on an adaptive null positioning scheme, and the second is based on a combination of the adaptive null positioning scheme and wider beampatterns. Simulations are performed using signals and propagation models obtained from multichannel binaural hearing aids recordings, including some in a mildly reverberant environment. Evaluations are done in terms of noise reduction and target distortion. Evaluation results illustrate the robustness of the two proposed designs to errors between the true and estimated directions of arrival for the target source, and to mismatch between the anechoic propagation models used for the beamformers designs and the reverberant propagation models used to generate the signals at the sensors or beamformer inputs. Both designs surpass the performance of standard binaural Minimum Variance Distortionless Response (MVDR) and binaural Generalized-Side Lobe Canceler (GSC) beamformers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".