Assessment of a feasible virtual acoustics method for testing hearing aids using the Hearing-Aid Speech Perception Index
Bibliographic record
Abstract
Significant differences have been found in hearing aid (HA) performance between laboratory and real world test environments. Virtual sound environments provide a degree of control and reproducibility which is lacking in real world testing but may require an impractical number of loudspeakers. We assessed the accuracy of a simulation approach in which sources’ direct sound is delivered by single loudspeakers while room acoustics are reproduced using low-order Ambisonics and a small number of loudspeakers. In a large office, we recorded binaural hearing aid output in response to sentence targets and babble noise presented at various levels and from various combinations of four loudspeakers surrounding a manikin. We measured the loudspeakers’ room impulse responses (IRs) using a 32-channel spherical microphone array (Eigenmike), and split the IRs into "direct sound" and "room sound" portions. In an anechoic chamber, the original acoustics were simulated using Ambisonics or discrete loudspeakers for each source’s direct portion and Ambisonics for the room portion. Ambisonic order and/or number of playback loudspeakers were also varied. HA output in the simulations was recorded using the manikin and assessed by comparing Hearing-Aid Speech Perception Index (HASPI) values computed on the simulation recordings with those made in the original room.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".