Comparison of Loudspeaker Placement Methods for Sound Field Reproduction
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Bibliographic record
Abstract
This paper presents a comparison between several loudspeaker placement methods for sound field reproduction (SFR). The goal of these placement methods is to reduce the SFR error under a power constraint by selecting suitable locations for the loudspeakers. The first method is based on singular value decomposition of the acoustic transfer function (ATF) matrix. Depending on the configuration, an ideal ATF matrix is created and, then, approximated by selecting the appropriate locations for the loudspeakers. Another method is based on the constrained matching pursuit (CMP) algorithm, in which candidate locations of the loudspeakers are selected iteratively to minimize the approximation error of the desired sound field. The third method is based on sparsity-promoting sound field approximation using the least absolute shrinkage and selection operator. Loudspeaker placements obtained using these methods are compared against benchmark configuration of uniformly distributed loudspeakers. The comparison indicates that for constrained power, the CMP-based placement has the least reproduction error.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it