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Record W4233485410 · doi:10.1121/1.4800424

Advanced hearing protection and auditory awareness in individuals with hearing loss

2013· article· en· W4233485410 on OpenAlexafffund
Christian Giguère, Chantal Laroche, Véronique Vaillancourt

Bibliographic record

VenueProceedings of meetings on acoustics · 2013
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of Ottawa
FundersDefence Research and Development Canada
KeywordsActive listeningSituation awarenessComputer scienceTask (project management)Hearing lossNoise (video)Hearing protectionAmbient noise levelSpeech recognitionAudiologyPsychologyEngineeringAcousticsArtificial intelligenceCommunicationMedicine

Abstract

fetched live from OpenAlex

In-ear and earmuff-type electronic protection devices are rapidly being introduced into the marketplace and deployed in noisy industrial workplaces and military settings. In these environments, workers must be sufficiently protected from noise while being able to maintain good communication abilities and situational awareness. Features such as level-dependent attenuation or amplification, noise reduction, and remote communication capabilities are commonly found in advanced devices. The benefits of these features depend on their complex interaction with the signal and noise characteristics, the hearing status and language proficiency of the workers, and the nature of the auditory task. Furthermore, advanced devices often have different modes of operation and several user-adjustable control settings. In this paper, the interaction between the gain control setting and the degree of hearing loss is investigated for two level-dependent devices in a speech recognition task in noise. At all gain settings, the level-dependent mode of the devices provided better speech recognition that passive attenuation for all groups of subjects, and performance often exceeded unprotected listening.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.703
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.259
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2013
Admission routes2
Has abstractyes

Explore more

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