Localization of novel reduced-annoyance audio interface sounds
Bibliographic record
Abstract
Foley et al. [JASA 151, 3189–3196 (2022)] have demonstrated that annoyance of auditory interface sounds can be reduced both by shortening the duration of upper harmonics and by applying percussive rather than flat amplitude envelopes. However, sounds in that study had a maximum frequency of 2400 Hz, which would likely affect localization based on spectral cues. In a setting with multiple interface devices (e.g., a multi-patient hospital ward), localization of interface sounds is a concern. We characterized normally hearing listeners' ability to localize a variety of candidate reduced-annoyance interface sounds (flat or percussive envelopes; durations between 360 and 1600 ms; all but one with uppermost harmonic limited to 2400 Hz) in quiet or at + 4-dB and −11-dB SNR in spatially diffuse multi-talker babble. Listeners stood at the center of a 360-degree loudspeaker array in a darkened anechoic chamber and used a head-pointing response to report the perceived location of each target. Decreasing SNR increased response variability and the rate of front/rear confusions. For all sounds with restricted bandwidth, front/rear confusions were frequent in the absence of head movements, but when head movements were initiated before target offset, confusions were substantially reduced. The results highlight the need to consider localizability when designing improved auditory interface sounds.
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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 | 0.000 | 0.000 |
| 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".