Improving the Detection of Melodic Sequences Through the Addition of Inharmonic Frequencies
Bibliographic record
Abstract
Our ability to detect and discriminate between auditory signals is crucial to our daily lives (Gundy, 1961), and is required in auditory warning systems for a variety of safety critical devices (Stanton & Edworthy, 2019) —such as hospital alarms (Sanderson et al., 2006). Recent research has focused on harmonicity as a salient dimension in signal detection (McPherson et al., 2022). Inharmonic sounds tend to be more attention grabbing (Bonin & Smilek, 2016), while higher pitch frequencies are less likely to conflict with ambient noise. This suggests the introduction of high, inharmonic frequencies could improve detectability. To assess whether this indicates binding of the inharmonic tones or additional information increasing detection, we measured accuracy and response time in two-alternative forced choice (2AFC) tasks, whereby participants decided whether a melodic sequence played in noise ascended or descended. The sequence included either congruent, incongruent, or an absence of higher harmonics. We found that the tracking additional harmonics improved mean accuracy by 6% and reduced mean response time by 72ms relative to their absence. The stationary harmonics reduced response time by 74ms, but also reduced accuracy by 13% relative to their absence.The results suggest that the additional harmonics can speed up reaction time, regardless of whether they are congruent. However, the increase in accuracy for the tracking condition relative to the decrease for stationary implies that discrimination depends on congruency. Without this, they may be a source of distraction, erroneously drawing attention away from the signal instead of supporting it. Overall, this supports an information rather than a binding mechanism of facilitation. Future experiments will further explore the role of binding in detection by varying amplitude envelope between the signal and higher harmonics. We can then infer how they could be added to current alarms to improve detectability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".