Performance on 2 tasks of the AIRS Test Battery of Singing Skills in Persons with Cochlear Implants
Bibliographic record
Abstract
Cochlear implants aim to maximize speech understanding, but to what extent do implants support musical activities? Here we focus on singing in 4 adults (age 38, 50, 59, 73 years) with cochlear implants who carried out the AIRS Test Battery of Singing Skills (ATBSS) (Cohen, et al., 2009). The ATBSS taps 7 singing skills including singing a familiar song, singing a favourite song, learning a new song, repeating melodic elements, and creating a new song. It also measures several verbal skills. The test was administered twice to 2 individuals and once to the others. Participants also completed a biographical questionnaire. One focus of analysis was singing the familiar son--in particular the 10 repetitions of the key-note (tonic). Variability (SD) in pitch of the 10 sung tonics ranged from 1.37 to 5.88 semitones, higher than for persons with normal hearing (0.55 semitones; obtained in a separate study of 20 normal-hearing non-musicians). Mean error of the sung tonic ranged from 0.29 to 8.06 semitones, for best to poorest performance (normal hearing mean was 1.58). Analysis of contour accuracy of 3-note sequences (triads) revealed a range of systematic success or failure across participants. Music played a role in the lives of each participant- one currently sang in a choir; another played guitar. The gap between the ability to sing with normal accuracy and the desire to make music highlights the importance of additional research to characterize further the singing abilities of those who have cochlear implants. Whether the current mismatch problems could be overcome through training remains an important question. Cohen, A. J., Armstrong, V., Lannan, M. & Coady, J. (2009). A protocol for cross-cultural research on acquisition of singing. Neurosciences and Music III-Disorders and Plasticity: Annals of the New York Academy of Science , 1169 , 112-115.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".