Bibliographic record
Abstract
Sounds can have a profound impact on the way people think and feel about the world around them. However, recent work suggests that hearing loss alters the way people feel about sounds. Specifically, people with hearing loss are less affected by sounds than their peers with normal hearing. Moreover, increasing the overall level of sounds does not restore emotional responses. Instead, when the volume is increased, listeners with hearing loss rate all sounds, even the “pleasant” ones, as unpleasant. Because hearing aids increase the overall level of sounds, there is significant clinical and scientific interest in evaluating the potential effect of hearing aids on emotional responses to sounds, which was the purpose of this study. Adults with mild to moderately-severe sensorineural hearing loss listened to a subset of sounds from a published corpus of common, non-speech sounds. Participants rated the degree to which a sound made them feel pleasant/unpleasant and also excited /calm. Participants rated each sound at a moderate (60 dB SPL) and a high (80 dB SPL) intensity. In addition, participants rated moderate intensity stimuli while wearing bilateral hearing aids programmed with conventional processing and non-linear frequency compression. A control group of participants with normal hearing was tested in the unaided conditions. Participants made subjective ratings using a published visual analog scale and a computer keypad. Consistent with previous work, listeners with hearing loss exhibited a reduced range of emotional responses. Neither hearing aid technology improved the range of responses. However, for specific signals, there were positive effects of hearing aids, particularly non-linear frequency compression. Some of the specific effects of hearing aid signal processing can be explained based on the acoustics of the stimuli and of the hearing aid technologies. These acoustic relationships will be discussed, as well as implications for future technological developments.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".