The Effect of the Audallion?? BEAMformer Noise Reduction Preprocessor on Sound Localization for Cochlear Implant Users
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of the Audallion BEAMformer noise reduction preprocessor on the sound localization ability of children fitted unilaterally with the Nucleus 22-channel cochlear implant. DESIGN: Eight children aged 11 to 14 yr participated. Using three arrays of six loudspeakers, each child was tested in a semi-reverberant sound proof booth representative of a small office environment. The six loudspeakers were positioned 30 degrees apart in the horizontal plane at a distance of 1 m, spanning 150 degrees either directly in front of or to the left or right of the subject. The stimulus to be localized was a 300 msec broadband noise with a rise/decay of 50 msec. Subjects were tested with the BEAMformer in four possible settings, including one single microphone control condition, with each of the three loudspeaker arrays. One block of 60 forced-choice speaker identification trials was presented for each of the 12 listening conditions. On each trial, the stimulus was emitted by one of the six loudspeakers, randomly selected. RESULTS: Results demonstrated that subjects were unable to discriminate among sound sources arrayed horizontally in space using the cochlear implant microphone alone, or in combination, with the BEAMformer microphone located on the other ear. Results also showed that using the BEAMformer did not bias the perception of spatial location. CONCLUSIONS: The localization of acoustic stimuli in pediatric cochlear implant users was unaffected by the use of the Audallion BEAMformer. The apparent origin of most sounds appears to be pulled toward the implanted ear. Further studies are needed to validate findings.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".