Audio augmentation of car journeys to improve occupants' well-being
Bibliographic record
Abstract
Car interiors are often designed with the aim of being as quiet as possible. This has the benefit of eliminating most of the unwanted sound such as engine or tyre noise, but it also blocks out environmental sounds which might be perceived as positive and even desirable. Bringing in some of these positive sounds - particularly of nature or human activity - could enhance the experience of both the driver and the passengers. The literature has shown that being exposed to pleasant soundscapes has the potential to aid recovery from stress and is associated with lower heart rate than being exposed to unpleasant soundscapes. Therefore, increasing pleasantness of the sound environment in the car could lead to improved well-being. We will report on an immersive audio-visual listening experiment investigating how listeners perceive journeys augmented with realistic soundscapes. To increase realism and ecological validity, the experiment uses spatial audio, 360-degree videos presented through a virtual reality headset, and a car seat with vibrations corresponding to the presented drive.
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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 | 0.001 | 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 | 0.002 |
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".