Proposing a Low-Cost, Transportable Horizontal Binaural Test Using Headphones
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.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".