Increasing Detectability and Reducing Annoyance of Alarm Design Using Acoustic Structures of Musical Instruments
Bibliographic record
Abstract
Auditory alarms play a crucial role in safety critical contexts such as medical devices. Traditionally, alarms utilize sustained harmonics that lack the temporal dynamics found in complex sounds which are not easily detectable unless played at a high volume, leading to increased annoyance (Foley et al., 2023). Here we explore how insights from musical instruments can be used to improve auditory alarms by balancing annoyance and detectability. We synthesized tones mimicking key properties of a musical triangle—an instrument known to “cut through” large ensembles without being annoying. We then tested two classes of stimuli, (1) a triangle inspired and (2) a standard alarm tone. The triangle stimuli feature twelve of the most prominent frequencies, amplitudes, and temporal variation from a recording of a triangle, including a percussive amplitude envelope. Standard tone follows conventional approaches with the fundamental at 261 Hz and four additional harmonics, shaped with a flat amplitude envelope. To compare the triangle to the standard tone, we conducted an in-person detection and an online annoyance rating experiment. For detection, participants indicated if the auditory stimulus is heard when presented a range of signal-to-noise (SNR) ratios. For annoyance, participants rated which tone is perceived as more annoying across SNRs in a two-alternative forced choice task. As expected, reductions in SNR led to reductions in annoyance X2(5, N = 4488) = 478.01, p .05. This suggests that features of musical instruments such as the triangle could provide useful insight for reducing alarm annoyance while preserving detectability.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".