Virtual acoustics in multimedia production—Beyond enhancing the acoustics of concert halls
Bibliographic record
Abstract
The paper describes a range of applications of virtual acoustics, the rendering of artificial acoustic spaces, which allow musicians to interact with ambient spaces created in real time. With a number of loudspeakers suitably distributed within a physical enclosure, such a projection system can be used to introduce a range of sound fields, which may effectively transform the acoustic environment to become a creative partner in multimedia production. A necessary component in this system is a low-latency, high-resolution multichannel convolution engine that converts a live audio signal into a structured ambient response, creating a scene in real time. Scenes can be changed to suit various goals of production and sonic narration. A number of techniques have been used to capture and modify impulse responses including temporal segmentation, shaping of magnitude envelope, noise reduction, spectral enrichment, time shifting and alignment, and parallel and sequential convolution. With these methods, artists may interact with novel acoustic responses as if they were musical instruments. Artists claim that many of the familiar as well as novel acoustic responses stimulate their creativity. Flexible variable virtual acoustics opens additional creative possibilities when employed as a component of a recording studio, either directly or when rendered over headphones.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".