A real-world Lexicon 960L reverberation chamber: Simulating a hardware reverberation unit in virtual acoustics
Bibliographic record
Abstract
The second generation of the Virtual Acoustic Technology (VAT) Laboratory at McGill University features a new real-time auralizer with a feedback canceller developed by CCRMA at Stanford University, allowing for the simulation of virtual acoustic environments with exceptionally high gain. This study is part of an ongoing research effort focused on integrating algorithmic reverberation tools designed for audio post-production into virtual acoustics at McGill University’s VATLab. Previous work has been done using impulse responses (IRs) captured from various acoustic spaces. In contrast, this study focuses on using IRs captured from the legendary Lexicon 960L hardware reverberation unit and using them in the VATLab for recording sessions with musicians. Various 5.1 multichannel presets have been captured as IRs and 3 groups of related 5 channel IRs have been loaded into the existing 15 speaker system of VATLab to simulate a real world, physical Lexicon 960L “environment” through virtual acoustics. Objective measurements following the ISO 3382-1 and 3382-2 standards in the VATLab have been performed to measure the effect of the physical room and analyze the effects of changing different algorithmic reverb parameters such as Diffusion, Early Level Master Control, Early Rolloff, Early or Reflection Delays on the simulated acoustical environments.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".