Spatial room responses measured with a diversity of loudspeaker sources
Bibliographic record
Abstract
Measuring room response using loudspeakers requires a consideration of their acoustic radiation pattern, position in each space, effective bandwidth, and dynamic range. Musical instruments produce sound output that varies considerably in directivity and complexity of room excitation. Groups of instruments and voices activate multiple room responses simultaneously from different locations. Therefore, the resulting audible outcome in a space is a spatial superposition of multiple staggered room responses developing in time, frequency, and amplitude. In a multi-year campaign measuring different indoor and outdoor spaces, impulse response experiments included room excitation by diverse electroacoustic sources built from single and multiple transducers setup to represent musical sources. An examination of the results shows that aural characteristics of rendered virtual rooms differ depending on the source excitation used. Utility of Spatial Room Impulse Responses aiming to elicit a sense of presence in an enclosure depends on choosing SRIRs that complement the direct sound of a musical source. Examples will be shown. Convolution processing using SRIRs offers the most effective perceptual transformation of sound superior to other techniques as it processes sound simultaneously in all domains: spatial, timbral, dynamic, and temporal.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".