On Crosstalk Cancellation and Equalization With Multiple Loudspeakers for 3-D Sound Reproduction
Bibliographic record
Abstract
People prefer to be able to enjoy spatial audio without wearing a headphone. Such a tethered device is anyway inconvenient and undesirable, if not cumbersome. Alternatively, 3D sound can be delivered to a listener with loudspeakers. However, crosstalk arises, and the rendered binaural signals are distorted by room reverberation when arriving at the listener's two ears, which lead to the need for a crosstalk cancellation and equalization (CTCE) system. Classical CTCE systems employ only two loudspeakers, and their performance is usually unsatisfactory in practice. While the idea of using more loudspeakers has been investigated, it was never shown why using more loudspeakers is theoretically more advantageous for CTCE. In this letter, we will study this problem and demonstrate that with two loudspeakers, only a least-squares (LS) solution can be obtained, while using multiple loudspeakers, we have more options: either an LS solution or an exact solution for perfect CTCE. These findings are justified by simulations using real impulse responses measured in the varechoic chamber at Bell Labs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".