The impact of variation in low-frequency interaural cross correlation on auditory spatial imagery in stereophonic loudspeaker reproduction
Bibliographic record
Abstract
Several attributes of auditory spatial imagery associated with stereophonic sound reproduction are strongly modulated by variation in interaural cross correlation (IACC) within low frequency bands. Nonetheless, a standard practice in bass management for two-channel and multichannel loudspeaker reproduction is to mix low-frequency musical content to a single channel for reproduction via a single driver (e.g., a subwoofer). This paper reviews the results of psychoacoustic studies which support the conclusion that reproduction via multiple drivers of decorrelated low-frequency signals significantly affects such important spatial attributes as auditory source width (ASW), auditory source distance (ASD), and listener envelopment (LEV). A variety of methods have been employed in these tests, including forced choice discrimination and identification, and direct ratings of both global dissimilarity and distinct attributes. Contrary to assumptions that underlie industrial standards established in 1994 by ITU-R. Recommendation BS.775-1, these findings imply that substantial stereophonic spatial information exists within audio signals at frequencies below the 80 to 120 Hz range of prescribed subwoofer cutoff frequencies, and that loudspeaker reproduction of decorrelated signals at frequencies as low as 50 Hz can have an impact upon auditory spatial imagery. [Work supported by VRQ.]
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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 | 0.008 |
| 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 | 0.000 |
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".