Effects of room-acoustic exposure on localization and speech perception in cocktail-party listening situations
Bibliographic record
Abstract
Given previous findings suggesting perceptual mechanisms counteracting the effects of reverberation in a number of listening tasks, we asked whether listening experience in a particular room can enhance localization and speech perception abilities in cocktail-party situations. Utilizing the CRM stimuli we measured listeners' abilities in: (1) identifying the location of a speech target given a (-22.5o, 0o, + 22.5o) talker configuration, (2) identifying the target color/number under co-located (0o, 0o, 0o) and spatially-separated (Ő22.5o, 0o, + 22.5o) configurations. Stimuli were presented in three types of artificial reverberation. All reverberation types had the same relative times-of-arrival and levels of the reflections (T60 = 400 ms, C50 = 14 dB; wideband) and varied only in the lateral spread of the reflections. Reverberated stimuli were presented via a circular loudspeaker array situated in an anechoic chamber. Listening exposure was varied by mixing or fixing the reverberation type within a block of trials. For the location identification task, exposure benefit decreased with increasing Target-to-Masker Ratio (TMR). No exposure effect was observed in the speech perception task at Ő4 to 10 dB TMRs, except in the separated, narrowest reverberation condition. Results will be discussed in relation to the different nature of the tasks and findings from other studies.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".