Determining the area of the sweet spot in a surround loudspeaker setup for various microphone techniques
Bibliographic record
Abstract
Several types of microphone techniques exist to record music performances for surround-sound reproduction. Variations between different techniques are found in the distance and angle between the microphones, and the choice of directivity patterns. All the arrays are targeted to produce an accurate spatial impression at the sweet spot. The aim of this investigation is to determine how different microphone techniques affect the size of the sweet spot, the area in which the spatial cues are reproduced with sufficient accuracy. In particular, the common belief that spaced techniques lead to larger sweet-spot areas than coincidence and near-coincident techniques is investigated. For this purpose, impulse responses (IR) of different microphone arrays are measured in a concert hall. Afterwards, test sounds are convolved with the measured IRs and presented through a surround loudspeaker setup in a control room. A dummy head is used to record the reproduced sound fields at different positions inside the listening area. In a psychoacoustic experiment, listeners are asked to rate the different recordings according to the spatial impression provided by different recording techniques. The results of the listening test will be presented and compared to signal analyzes of a binaural model.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".