Real Rooms vs. Artificial Reverberation: An evaluation of actual source audio vs. artificial ambience
Bibliographic record
Abstract
Many spatial audio researchers and content producers agree that the best source material for height channels in immersive audio is provided by the capture of actual elevated channels in the room. Particularly for music recording, this technique is preferred as opposed to signal processing, providing a more natural and realisti impression of immersion. While previous work has proven this to be the case in the front channels of various 3D playback systems such as 22.2, the content of the rear height channels has not been specifically evaluated. Multichannel audio recording, specifically 3D recording can be a cumbersome task as the channel counts expand - and so the question arises - is it really necessary to capture discrete rear height information? This research compares four height channel capture points compared to two capture points applied to the front height channels in conjunction with artificial reverberation in the rear channels. A two-part study is employed - the first is a simple ABX test to determine discriminability between the real rooms and the artificially generated version. Part two is a preference test, based on several standard acoustic/perceptual descriptors, revealing the subtle differences between real and artificial rear height channel information.
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 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 | 0.003 | 0.011 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".