Capturing the acoustic response of historical spaces for interactive music performance and recording
Bibliographic record
Abstract
Performers engaged in musical recording while they are located in relatively dry recording studios generally find their musical performance facilitated when they are provided with synthetic reverberation. This well established practice is extended in the project described here to include highly realistic virtual acoustic recreation of original rooms in which Haydn taught his students to play pianoforte. The project has two primary components, the first of which is to capture for posterity the acoustic response of such historical rooms that may no longer be available or functional for performance. The project’s second component is to reproduce as accurately as possible the virtual acoustic interactions between a performer and the re-created acoustic space, as performers, during their performance, move relative to their instrument and the boundaries of surrounding enclosure. In the first of two presentations on this ongoing project, the method for measurement of broadband impulse responses for these historical rooms is described. The test signal is radiated by a group of omnidirectional loudspeakers approximating the layout and the complex directional radiation pattern of the pianoforte, and the room response is sampled by a spaced microphone array. The companion presentation will describe the method employed for virtual acoustic reproduction for the performer.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".