Binaural Headphone Monitoring to Enhance Musicians’ Immersion in Performance
Bibliographic record
Abstract
Musicians face challenges when using stereo headphones to perform with one another, due to a lack of audio intelligibility and the loss of their usual benchmarks. Also, high levels of click tracks in headphone mixes hinder performance subtleties and harm performers’ aural health. This chapter discusses the approaches and outcomes of eight case studies in professional situations that aimed at comparing the experiences of orchestra conductors and instrumentalists while monitoring their performances through binaural versus stereo headphones. These studies assessed three solutions combining augmented and mixed reality technologies that include binaural with head tracking to conduct a large film-scoring orchestra and jazz symphonic with a click track; binaural without head tracking to improvise in trio or on previously recorded takes in the studio; and active binaural headphones to record diverse genres on a click track or soundtrack. Findings concur to show that better audio intelligibility and recreated natural-sounding acoustics through binaural rendering enhance performers’ listening comfort, perception of a realistic auditory image, and musical expression and creativity by increasing their feeling of immersion. Findings also demonstrate that the reduction of source masking effects in binaural versus stereo headphone mixes enables performers to monitor less click track, and therefore protect their creative experience and aural health.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".