MétaCan
Menu
Back to cohort
Record W2024808359 · doi:10.1121/1.3458843

Subjective quantification of earplug occlusion effect using external acoustical excitation of the mouth cavity

2010· article· en· W2024808359 on OpenAlexaff
Cécile Le Cocq, Frédéric Laville, Christian Gargour

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2010
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsOcclusionAcousticsPerceptionAudiologyEar canalLimit (mathematics)Computer scienceMathematicsPsychologyMedicinePhysicsSurgery

Abstract

fetched live from OpenAlex

Occlusion of the ear canal by hearing aids or hearing protectors often results in an occlusion effect, which creates a discomfort to wearers in that it changes their perception of their own voice. As no account was found in the literature on the quantification of this subjective voice occlusion effect, an experimental method is proposed based on the use of an artificial sound source emitting within the subject's mouth to replace his own voice. A block diagram is constructed to identify the different internal sound path components involved in the perception of one's own voice and is used to show that the subjective voice occlusion effect is the weighted energy summation of two components. The first component, the voice air and body conduction occlusion effect for which data is obtained from the experiments reported in the present paper, constitute the lower limit of the subjective voice occlusion effect. The second component, the voice body conduction occlusion effect for which data is available in the literature, constitutes the upper limit. From these limits, order of magnitudes for subjective voice occlusion effect intervals are estimated to be [+5+20] dB below 2000 Hz and [-10+5] dB above 2000 Hz.

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)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.470
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.275
Teacher spread0.263 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicSpeech and Audio ProcessingFrench-language works237,207