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Record W4200567666 · doi:10.3390/audiolres11040061

Verification of a Mobile Psychoacoustic Test System

2021· article· en· W4200567666 on OpenAlexafffund
Jordana Costa Soares, Sangamanatha Ankmnal Veeranna, Vijay Parsa, Chris Allan, Winnie Ly, Minh Duong, Paula Folkeard, Sheila Moodie, Prudence Allen

Bibliographic record

VenueAudiology Research · 2021
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychoacousticsBinaural recordingAuditory systemComputer scienceAudiologyEncoding (memory)Speech recognitionPsychologyPerceptionMedicineNeuroscienceArtificial intelligence

Abstract

fetched live from OpenAlex

Many hearing difficulties can be explained as a loss of audibility, a problem easily detected and treated using standard audiological procedures. Yet, hearing can be much poorer (or more impaired) than audibility predicts because of deficits in the suprathreshold mechanisms that encode the rapidly changing, spectral, temporal, and binaural aspects of the sound. The ability to evaluate these mechanisms requires well-defined stimuli and strict adherence to rigorous psychometric principles. This project reports on the comparison between a laboratory-based and a mobile system's results for psychoacoustic assessment in adult listeners with normal hearing. A description of both systems employed is provided. Psychoacoustic tests include frequency discrimination, amplitude modulation detection, binaural encoding, and temporal gap detection. Results reported by the mobile system were not significantly different from those collected with the laboratory-based system for most of the tests and were consistent with those reported in the literature. The mobile system has the potential to be a feasible option for the assessment of suprathreshold auditory encoding abilities.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.276
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
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 head0.128
GPT teacher head0.427
Teacher spread0.300 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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