Design of a System to Measure Spatial Sound Localization Abilities
Bibliographic record
Abstract
Hearing ability declines with age. Specifically, it has been shown that sound localization ability becomes increasingly unreliable. This paper describes the design of a system that simulates spatial sound sources to be presented to the user via headphones. Simulation of spatial sound sources are calculated using four increasingly complex methods; (1) Phase adjustment simulation, (2) Amplitude adjustment simulation, (3) Amplitude + Phase adjustment simulation, and (4) Head Related Transfer Function (HRTF) adjustment simulation. The preliminary validation experiment is presented in order to evaluate if the system's simulated sources presented via noise cancelling headphones is comparable to previous work using simulated sources presented via a loudspeaker array surrounding the user. Eight participants were asked to report the perceived direction of the source for each simulation method. Overall the amplitude simulation performed the best (47.5% accuracy within a ±30° window), however a high accuracy is not needed for these simulation methods to be relevant. Implementing these methods to differentiate between population groups is the main goal. A secondary repeatability experiment was done using the amplitude simulation, which suggests that the method is suitable give the ±30° window constraint. Future implementations are proposed to evaluate if the user interface and accompanying user feedback is able to identify older adults from younger adults. This may lead to a measure of decreasing hearing abilities associated the aging process.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".