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Record W2044963496 · doi:10.1121/1.4877125

Measurement of hearing-protector attenuation using auditory steady state responses

2014· article· en· W2044963496 on OpenAlexaff
Olivier Valentin, Michael Sasha John, Frédéric Laville

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2014
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsUniversity of TorontoÉcole de Technologie Supérieure
Fundersnot available
KeywordsAttenuationHeadphonesAudiologyAcousticsMicrophoneNoise (video)LoudspeakerMasking (illustration)Binaural recordingComputer scienceMedicinePhysicsOptics

Abstract

fetched live from OpenAlex

There is a need to assess hearing protection device (HPD) attenuation to ensure that individuals receive adequate protection from noise. Present methods of attenuation measurement have limitations. Objective measurements such as field microphone in real ear (F-MIRE) do not assess bone conducted sound. Psychophysical measurements such as real ear attenuation at threshold (REAT) are biased due to the low frequency masking effects from test subjects' physiological noise, and the variability of measurements based on subjective response. We explored using auditory steady state responses (ASSR) as a technique that may overcome these limitations. ASSRs were recorded in ten normal hearing adults, using both “normal” and “occluded” conditions. Stimuli included both narrow band noises and pure tones (500 and 1000 Hz), amplitude modulated at 40 Hz. Stimuli were presented through either loudspeakers or headphones, at 45, 55, and 65 dB SPL. “Physiological attenuation” was calculated as the difference between ASSR values (both amplitude and phase) for normal and occluded conditions. Physiological attenuation estimates were compared to in-ear (objective) and psychophysical (subjective) measurements. Grand mean ASSR data complied well with in-ear and subjective measurements. Further work is needed to provide accurate assessment of ASSR-based physiological attenuation for individual subjects.

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)0.005
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.841
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
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 integrity0.0000.001
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 head0.068
GPT teacher head0.363
Teacher spread0.295 · 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
Published2014
Admission routes1
Has abstractyes

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