Source sparsity control of sound field reproduction using the elastic-net and the lasso minimizers
Bibliographic record
Abstract
Sound field reproduction is aimed at the reconstruction of a sound pressure field in an extended area using dense loudspeaker arrays. In some circumstances, sound field reproduction is targeted at the reproduction of a sound field captured using microphone arrays. Although methods and algorithms already exist to convert microphone array recordings to loudspeaker array signals, one remaining research question is how to control the spatial sparsity in the resulting loudspeaker array signals and what would be the resulting practical advantages. Sparsity is an interesting feature for spatial audio since it can drastically reduce the number of concurrently active reproduction sources and, therefore, increase the spatial contrast of the solution at the expense of a difference between the target and reproduced sound fields. In this paper, the application of the elastic-net cost function to sound field reproduction is compared to the lasso cost function. It is shown that the elastic-net can induce solution sparsity and overcomes limitations of the lasso: The elastic-net solves the non-uniqueness of the lasso solution, induces source clustering in the sparse solution, and provides a smoother solution within the activated source clusters.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".