The influence of changes in diffusive acoustical treatment on spatial imagery associated with multichannel sound reproduction
Bibliographic record
Abstract
Listening experience in the newly constructed, largely diffuse small room at Blackbird Studios has led to the design of a new research space for the Sound Recording program in McGill University’s Schulich School of Music. The space was created to allow for experimental investigation of the influence of changes in diffusive acoustical treatment on variation in auditory imagery associated with multichannel sound selections while holding listener location and orientation constant relative to a fixed five-channel loudspeaker array. Preliminary tests suggest that a number of benefits result when low-amplitude indirect sound is provided by diffusive room acoustic treatment within the first 30 ms of the arrival of the loudspeaker signals. Both for conventional multitrack mixes, and for recordings made using multichannel microphone arrays, these benefits include greater Listener EnVelopment (LEV), improved segregation of simultaneously sounding musical sources, and more solid phantom images of virtual sources, especially those located to the listener’s extreme sides between front and rear loudspeakers (between 30 and 110 deg of azimuth to the left and right). This paper will summarize those results, and describe the specially designed system of variable acoustic treatment that enables such direct experimental investigation. [Work supported by Canada Foundation for Innovation.]
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".