Bibliographic record
Abstract
An adjustable filter-bank based algorithm for digital speech processing "DSP" has been developed for hearing aid systems. This one was endowed with great flexibility and could be used both in cochlear prostheses by enabling to stimulate properly cochlea's nervous cells of totally or profoundly deaf patients, and in conventional hearing aids by providing different possibilities in searching patient comfort. Unlike other similar algorithms, programming handiness was provided to select basic speech characteristics to be considered for patient hearing. The implementation of this filter-bank algorithm on cochlear-prosthesis' DSP-board enables to generate and to control electrical stimulating pulses. In conventional hearing aids driven by DSP, a filter bank-based algorithm permits an ease adjustment of speech amplification, which is fully programmable within the considered sounds' spectrum. In each device, a programmable spectrum cut-up permits to adjust filters' bands relatively to patient's pathology. Programming via a host computer enable flexibility in speech amplification for conventional hearing aids and in cochlea's stimulation for cochlear prostheses. It combines handiness, ease of use and safety features to help meet individual's diverse needs. A computer illustration, based on spectrum cutting-up, was designed to identify filters' outputs. Hence, with this visual measure, clinicians could set up experiments for adjusting correctly hearing aid's operation-parameters.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".