Musical practice in audio augmented reality: Testing virtual acoustics using reverb convolution via bone conduction headphones
Bibliographic record
Abstract
Bone conduction headphones primarily transmit the audio signal directly to the inner ear. By not obstructing the ear canal, the system facilitates the perception of two layers of sound: a seamless integration of unmediated and virtual sound, enhancing the realism of the latter in an audio augmented reality experience. Our study aims to evaluate the impact of virtual acoustics on the performance of professional musicians. This paper mainly investigates the effect of a real-time convolution-based system that convolves sounds produced by musicians and delivers it to them via bone conduction headphones during musical performance. We investigate the impact of this system on musical practices and compare it to four other practice conditions: (1) an acoustically treated studio, (2) reverb convolution via traditional air conduction headphones, (3) reverb convolution via a loudspeaker-based virtual acoustic system, and (4) a real concert hall, where impulse responses were used for auralization in the virtual acoustics simulation. Data collected from musicians during both ensemble and solo performances—including EEG readings, performance analysis from audiovisual recordings, and surveys—provide, in our knowledge, insights never been conducted before on this research topic.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".