The effects of acoustical treatment on lateralization of low-frequency sources
Bibliographic record
Abstract
Recently, the standard of using a single low-frequency driver in stereophonic sound reproduction systems has come into question. Though it is accepted that lateral discrimination and localization of signals is possible well into the subwoofer frequency range, the use of multiple subwoofers in small reverberant rooms remains of questionable value. While inter-aural level differences (ILDs) are negligible at low frequencies, source lateralization is possible at low frequencies by virtue of inter-aural time differences (ITDs). But when such reproduction is attempted in small rooms, strong early reflections and resonances associated with room modes can cause erroneous ITD information to be detected by a listener, thereby compromising a listener’s ability to accurately locate the source of a low-frequency sound. Acoustical treatment can be employed to reduce the level of early reflections and low-frequency ringing associated with sharp resonant modes in small rooms. Such acoustical treatment often results in more accurate reproduction of ITDs, which enables more accurate localization of sound sources in the horizontal plane. This study investigated changes in measured interaural phase differences after a treatment scheme using both diaphragmatic and Helmholtz-style absorbers. The results show the viability of using multiple low-frequency drivers given adequate acoustical treatment of the reproduction space.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".