Musical timbre with varied amplitude envelope improves efficacy in auditory alarms
Bibliographic record
Abstract
Current alarm standards used in safety critical environments (e.g., medical alarms used in hospitals) suffer from a myriad of complications with detectability, annoyance, and alarm fatigue affecting the wellbeing of patients and staff. To a large extent, these are based on the same simplistic, temporally invariant tones. Here we explore how insights from the acoustic properties of the musical triangle can aid in detection, reducing overall levels hence reducing annoyance ratings. Two tones are used, (1) a standard tone similar to those used in current medical devices, and (2) a tone synthesized based on the spectral-temporal structure of a concert triangle. We conducted a detection experiment where participants indicated if the auditory stimulus is heard when presented a range of signal-to-noise (SNR) ratios and a two-alternative force choice task to measure annoyance ratings. Although reductions in SNRs reduced detectability for the standard tone, similar deductions had no meaningful effect on detectability of tones modeled off the musical triangle. Crucially, we identified a number of triangle inspired tones which are both less annoying and more detectable than standard tones. This suggests that these more complex sounds can reduce annoyance without harming detection, offering useful insight to medical device sound design.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".