Proposing a Low-Cost, Transportable Horizontal Binaural Test Using Headphones
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Binaural hearing plays a significant role in auditory perception, spatial awareness, and sound source differentiation. Studies have linked cognitive decline, traumatic brain injuries, and neurodegenerative disease with decline in binaural performance, and quantification thereof may provide key information related to early onset of such diseases. Current horizontal binaural tests require multiple external speakers and an anechoic chamber, preventing broad clinical deployment, especially in remote communities. Furthermore, they use design parameters that differ widely. We hereby aim to develop a portable, easy-to-perform binaural hearing performance test, as well as identifying the ideal design parameters used in current literature, including audio prompt type, frequency, duration, and modality (speaker vs. headphone). Results indicate that a voice-form audio, with a sampling frequency of 1 kHz combined with a 4-second duration yields the best outcomes. Moreover, comparisons between speaker modality tests and our proposed headphone modality test using a Head Related Transfer Function (HRTF) reveal a high level of agreement between performances. Notably, the proposed headphone test mitigates sources of bias such as informed guessing due to visual cues, memorization of source locations, and movement of the head during tests. The findings establish the potential of a low-cost, easily accessible binaural performance test applicable across diverse settings. This research contributes insights into the design and implementation of binaural tests, with implications for fields such as audiology and neuroscience.
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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 it