Evaluating a Hearing Loop Implementation for Live Orchestral Music
Bibliographic record
Abstract
Introduction: Live music creates a sense of connectedness in older adults, which can help alleviate the social isolation frequently associated with hearing loss and aging. However, most hearing aid (HA) users are dissatisfied with the sound quality for live music and rate sound quality as important to them. Assistive listening systems are frequently independent of a user’s HAs and fall short in tailoring to each individual’s hearing loss. The present study thus tested whether the use of a hearing loop would improve sound quality during an orchestral concert. Method: Participants with symmetrical moderate-to-severe hearing loss were assigned to use Sonova-provided HAs with a telecoil (n = 20) or their own HAs (n = 8) without a telecoil during a performance by the Hamilton Philharmonic Orchestra. We changed loop input to use microphones from the stage, balcony, or no feed every 5 minutes and collected associated sound quality and naturalness ratings. Results: Sound quality and naturalness ratings were highly related (rRM = .81), though each provided unique insight. Repeated Measures ANOVA found significant differences among the loop feed conditions for sound quality and naturalness, with the No Feed condition significantly outperforming the House condition on sound quality (t(18) = -3.73, adj-p = .005) and naturalness (t(18) = -4.15, adj-p = .002). Mixed effects models allowed us to retain the richness of a repeated observation dataset and provided point estimates of the overall quality and naturalness among conditions; however, assumption violations of normality and homoskedasticity prevent further interpretation. Conclusions: Though hearing aid-integrated assistive listening systems are a promising option for improving live music for people with hearing loss, a hearing loop does not seem to be crucial for orchestral music. Future directions include improving lyric understanding for music with vocals and customizing user experience via Bluetooth Low Energy Audio systems.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".