A method to virtually extend reverberation time of measured impulse responses without losing room coloration
Bibliographic record
Abstract
For various audio and acoustical applications, it is useful to modify the reverberation time of a measured room impulse response. For example, it can be interesting for creating various modified impulse responses for convolution reverbs in video games or sound designs. With this in mind, it is proposed to modify an impulse response reverberation time while keeping the other reverberation characteristics, such as the frequency response, unaffected. Knowing how a typical impulse response is related to the room characteristics, we propose a new method. First, the impulse response of a room is measured. Then, the reverberation time is evaluated. This reverberation time is next approximated by Sabine-Eyring equation, resulting in an absorption coefficient. A new absorption coefficient is then manually provided to reach a new target reverberation time. Finally, the late reverberation of the original impulse response is convolved with a gaussian white noise to the desired length and stitched with appropriate decaying envelopes to the end of the initially measured impulse response. This paper illustrates that the method works based on simulations and that the modified impulse response is realistic and can be related to the actual room.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".