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Record W1996594343 · doi:10.1044/1092-4388(2006/078)

Song Recognition by Children and Adolescents With Cochlear Implants

2006· article· en· W1996594343 on OpenAlexafffund
Tara Vongpaisal, Sandra E. Trehub, E. Glenn Schellenberg

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2006
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchHospital for Sick Children
KeywordsAudiologyCochlear implantPsychologyCochlear implantationDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

PURPOSE: To assess song recognition and pitch perception in prelingually deaf individuals with cochlear implants (CIs). METHOD: Fifteen hearing children (5-8 years) and 15 adults heard different versions of familiar popular songs-original (vocal + instrumental), original instrumental, and synthesized melody versions-and identified the song in a closed-set task (Experiment 1). Ten CI users (8-18 years) and age-matched hearing listeners performed the same task (Experiment 2). Ten CI users (8-19 years) and 10 hearing 8-years-olds were required to detect pitch changes in repeating-tone contexts (Experiment 3). Finally, 8 CI users (6-19 years) and 13 hearing 5-year-olds were required to detect subtle pitch changes in a more challenging melodic context (Experiment 4). RESULTS: CI users performed more poorly than hearing listeners in all conditions. They succeeded in identifying the original and instrumental versions of familiar recorded songs, and they evaluated them favorably, but they could not identify the melody versions. Although CI users could detect a 0.5-semitone change in the simple context, they failed to detect a 1-semitone change in the more difficult melodic context. CONCLUSION: Current implant processors provide insufficient spectral detail for some aspects of music perception, but they do not preclude young implant users' enjoyment of music.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.036
GPT teacher head0.335
Teacher spread0.299 · 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 designObservational
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

Citations100
Published2006
Admission routes2
Has abstractyes

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