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Record W4403267950 · doi:10.3397/in_2024_1830

Assessment of transparent alternative listening devices' effects on localization cues using head-related transfer function measurements

2024· article· en· W4403267950 on OpenAlexaff
Alexis Pinsonnault-Skvarenina, Fabien Bonnet, Hugues Nélisse, Jérémie Voix

Bibliographic record

VenueNOISE-CON proceedings · 2024
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travailÉcole de Technologie Supérieure
Fundersnot available
KeywordsActive listeningComputer scienceHearing aidTorsoSet (abstract data type)AudiologyMedicinePsychologyCommunication

Abstract

fetched live from OpenAlex

The past few years have seen a meteoric rise in technological advancements in the hearing health industry. New alternative listening devices, often referred to as "hearables", aim to become real "bionic ears" offering hearing protection, amplification, monitoring and even biosensing functionalities. With the recent approval of the "Over-the-Counter Hearing Aid Act" in the United States and the introduction of so-called "OTC" hearing aids, these alternative listening devices now have the potential to revolutionize the world of traditional auditory amplification. Since these devices may be used by individuals with hearing loss, it is important to consider how they might influence spatial auditory awareness. Therefore, this paper studies the effect of alternative listening devices on localization cues using the Head Related Transfer Function (HRTF). In this purpose, we measured the HRTFs on a Head and Torso Simulator (HATS) with and without digital listening devices. To evaluate the acoustic performance of these devices, the devices were set into their "acoustically transparent" mode. These measures enable a preliminary characterization of the effect of hearables and OTC devices on localization cues.

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)0.000
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: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.080
GPT teacher head0.343
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

Citations0
Published2024
Admission routes1
Has abstractyes

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