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Record W1983310611 · doi:10.1121/1.4805758

Development of a new series of tests to assess the effectiveness of hearing aids for the perception of music

2013· article· en· W1983310611 on OpenAlexaff
Martin Kirchberger, Frank Russo, Peter Derleth, Markus Hofbauer

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2013
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTimbreAudiologyHearing aidPerceptionPsychologyHearing impairedSpeech recognitionComputer scienceMusicalMedicine

Abstract

fetched live from OpenAlex

A new series of tests has been designed to assess the effectiveness of hearing aids for the perception of music. Within each subtest, discrimination thresholds for low-level acoustic dimensions are determined adaptively using a 2AFC method within the context of a musical judgment regarding melody, harmony, timbre or meter. The presented test stimuli are synthesized and either unprocessed or processed by different hearing aid signal processing algorithms before being played back via loudspeaker. The battery will be used to evaluate different hearing aid algorithms with regard to their benefit for functional hearing in music. A group of six normal hearing control participants (6.7 dB HL) and five hearing impaired participants (34 dB HL) each performed the melody subtest and the harmony subtest twice. The hearing impaired subjects had higher discrimination thresholds than the control group. A comparison of the results from both administrations suggests that these two subtests have good test-retest reliability.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.054
GPT teacher head0.313
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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 routes1
Has abstractyes

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