Better-ear glimpsing with symmetrically-placed interferers in bilateral cochlear implant users
Bibliographic record
Abstract
For a frontal speaker in spatially symmetrically placed maskers, normal hearing (NH) listeners can use an optimal “better-ear glimpsing” strategy selecting time-frequency segments with favorable signal-to-noise ratio in either ear. It was shown that for a monaural signal, obtained by an ideal monaural better-ear mask (IMBM), NH listeners can reach similar performance as in the binaural condition, but interaural phase differences at low frequencies can further improve performance. In principle, bilateral cochlear implant (BiCI) users could use a glimpsing strategy; however, they cannot exploit (temporal fine structure) interaural phase differences. Here, speech reception thresholds of NH and BiCI listeners were measured in two symmetric maskers (±60°; speech-shaped stationaty noise, non-sense speech, single talker) using head-related transfer functions and headphone presentation or direct stimulation in BiCI listeners. Furthermore, a statistically independent masker in each ear, diotic presentation with IMBM processing, and a noise vocoder based on the BiCI electrodogram was used in NH. Results indicate that NH with vocoder and BiCI listeners show a strongly reduced binaural benefit in the ±60° condition relative to the co-located condition when compared to NH. However, both groups greatly benefit from IMBM processing (as part of the CI stimulation strategy). Individual differences are compared.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".