The binaural performance of a cross-talk cancellation system with matched or mismatched setup and playback acoustics
Bibliographic record
Abstract
Cross-talk cancellation is a method for synthesizing virtual auditory space using loudspeakers. One implementation is the "Optimal Source Distribution" technique [T. Takeuchi and P. Nelson, J. Acoust. Soc. Am. 112, 2786-2797 (2002)], in which the audio bandwidth is split across three pairs of loudspeakers, placed at azimuths of +/-90 degrees, +/-15 degrees, and +/-3 degrees, conveying low, mid, and high frequencies, respectively. A computational simulation of this system was developed and verified against measurements made on an acoustic system using a manikin. Both the acoustic system and the simulation gave a wideband average cancellation of almost 25 dB. The simulation showed that when there was a mismatch between the head-related transfer functions used to set up the system and those of the final listener, the cancellation was reduced to an average of 13 dB. Moreover, in this case the binaural interaural time differences and interaural level differences delivered by the simulation of the optimal source distribution (OSD) system often differed from the target values. It is concluded that only when the OSD system is set up with "matched" head-related transfer functions can it deliver accurate binaural cues.
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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) | 0.000 | 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) | 0.002 | 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".