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Record W4400285407 · doi:10.1121/10.0027607

Musical timbre with varied amplitude envelope improves efficacy in auditory alarms

2024· article· en· W4400285407 on OpenAlexaff
Andres E. Lopez, Joseph J. Schlesinger, Michael Schutz

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2024
Typearticle
Languageen
FieldMedicine
TopicHealthcare Technology and Patient Monitoring
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTimbreEnvelope (radar)Pitch (Music)Speech recognitionAudiologyMusicalAcousticsAmplitudeMusical tonePsychologyComputer scienceArtPhysicsMedicineTelecommunicationsVisual arts

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.733
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.294
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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