Bibliographic record
Abstract
Hearing assistance is to improve sound intelligibility, and takes on many forms, from the traditional earborn hearing aids to cellphone apps for connected earphones. These systems are useful for those with hearing impairment or for those struggling with background noise, such as when the cocktail effect is compromised by the spoken language not being sufficiently familiar. While in-line time-signal processing can help, the best way forward is to use multiple microphones to harness the power of array signal processing. Arrays allow the simultaneous enhancement of the wanted sound and suppression of interfering sounds, as long as the interferers are in different locations from the wanted sound. This paper demonstrates how the performance of the main beamforming algorithms can be explored very conveniently using MATLAB. The algorithms are deployed and evaluated here using a simple representative scenario - the geometric arrangement of wanted and interfering signals and the array elements. Here we use a linear array and a circular array, each of ten elements, that can be mounted on the head; a single wanted signal source, and just a couple of interfering sources. When the number of array elements is greater than the number of sources, there are enough signalprocessing degrees of freedom to strongly improve the wanted signal-to-interference ratio.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".