Reverberation limits the release from informational masking obtained by differences in fundamental frequency and in spatial location
Bibliographic record
Abstract
Differences in fundamental frequency (ΔF0s) and differences in spatial location (ΔSLs) between competing talkers can substantially enhance intelligibility of a target voice in a typical cocktail-party situation. Reverberation is generally detrimental to the use of these two cues, but it is possible to create laboratory conditions where reverberation should not disrupt the release from energetic masking produced by ΔF0s and ΔSLs. Two masker types were used: a 2-voice speech masker and a non-linguistic masker (primarily energetic) matched in long-term excitation pattern and broadband temporal envelope to speech maskers. Speech reception thresholds were measured either in an adaptive procedure with unpredictable sentences or with the coordinate response measure at fixed target-to-masker ratios, in conditions with or without ΔF0s, and with or without ΔSLs, against the two masker types in anechoic and reverberant conditions. Both methods provided a similar pattern of results. In the presence of non-linguistic maskers, ΔF0s and ΔSLs provided masking releases which, as intended, were robust to reverberation. Larger masking releases were obtained for speech maskers, presumably due to the additional informational component, but critically, they were reduced by reverberation. Several interpretations will be discussed at the meeting.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".