Visualizing Auditory Spatial Imagery of Multi-channel Audio
Bibliographic record
Abstract
To describe a multichannel audio experience in terms of its spatial features requires us to consider sound imagery in terms of precedent sound. We mean precedent sound to be that part of a phantom sound image that contains spatial information about the virtual sound source. We have developed and tested a Graphical User Interface (GUI) to allow a listener to describe where they hear both precedent and environment-related sound in an audio scene. The GUI has previously been used as a tool for describing where we hear the precedent sound in two-channel sound reproduction, and we now extend the experimental paradigm to investigate phantom imagery for a multichannel loudspeaker arrangement. We present a category system for describing the spatial sound attribute “definition”, and have tested the GUI using 5 loudspeakers arranged according to BS-775 to replay multi-channel sound recordings of three different musical pieces (two duets and one solo). Graduate Tonmeister students used the GUI to describe these sound scenes, and a variety of statistical analyses are used to visualize auditory spatial imagery. USHER AND WOSZCZYK VISUALIZING AUDITORY SPATIAL IMAGERY
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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 | 0.000 | 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) | 0.004 | 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".