The added value of radio-acoustic virtual environment
Bibliographic record
Abstract
As hearing protection devices (HPDs) also attenuate voice, workers tend to remove them to talk to each other, disabling their protection. Radio-acoustic virtual environment (RAVE) proposes an ideal situation, where workers can communicate in noisy environments while being protected. The voice is recorded with in-ear microphones (IEM), denoised, and transmitted within a certain communication radius determined by the talker's vocal effort. The audio is played at a comfortable level with directionality. Many articles addressed elements necessary for RAVE, such as voice activity detection [N. Lezzoum et al., in IEEE JCE, 2014, pp. 737–744], wearer induce disturbances detection [F. Bonnet et al., in JERGON, 2019, pp. 102862], and communication radius [R. Bouserhal et al., in JSLHR, 2017, pp. 3393–3403]. This pioneering research integrated them and tested a mock-up version of RAVE in live scenarios. In groups of three, twenty-one participants completed manual tasks that required communication. They used RAVE's mock-up and a broadcasting push-to-talk device, with and without noise presence, totaling four scenarios. Participants completed a questionnaire after each scenario, and their speech and motion were recorded. This research contributed with a mock-up version of RAVE with improved signal treatment for real-time purposes. Our results confirm RAVE's added value and illustrates its potential.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".