Low-frequency interaural cross correlation discrimination in stereophonic reproduction of musical tones
Bibliographic record
Abstract
By industry standard (ITU-R. Recommendation BS.775-1), multichannel stereophonic signals within the frequency range of up to 80 or 120 Hz may be mixed and delivered via a single driver (e.g., a subwoofer) without significant impairment of stereophonic sound quality. The assumption that stereophonic information within such low-frequency content is not significant was tested by measuring discrimination thresholds for changes in interaural cross-correlation (IACC) within spectral bands containing the lowest frequency components of low-pitch musical tones. Performances were recorded for three different musical instruments playing single notes ranging in fundamental frequency from 41 Hz to 110 Hz. The recordings, made using a multichannel microphone array composed of five DPA 4006 pressure microphones, were processed to produce a set of stimuli that varied in interaural cross-correlation (IACC) within a low-frequency band, but were otherwise identical in a higher-frequency band. This correlation processing was designed to have minimal effect upon other psychoacoustic variables such as loudness and timbre. The results show that changes in interaural cross correlation (IACC) within low-frequency bands of low-pitch musical tones are most easily discriminated when decorrelated signals are presented via subwoofers positioned at extreme lateral angles (far from the median plane). [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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".