The effects of diffuse noise and artificial reverberation on listener weighting of interaural cues in sound localization
Bibliographic record
Abstract
The reliability of interaural time and level difference (ITD, ILD) sound location cues can be degraded by noise or reverberation. In this study, we determined how weighting of ITD and ILD varied with signal-to-noise ratio (SNR) in the presence of interaurally uncorrelated background noise and with direct-to-reverberant ratio (DRR) in the presence of artificial reverberation (generated by convolving the target signal with an interaurally uncorrelated pair of impulse responses created by multiplying Gaussian noise with a decaying exponential, RT60 = 500 ms). Wideband (0.5-16 kHz) 100-ms noise-burst targets were presented over headphones using individual head-related transfer functions. ITD and ILD were manipulated by attenuating or delaying the sound at one ear (by up to 300 μs or 10 dB), and cue weighting was computed by comparing localization response bias to imposed cue bias. Wideband (0.5-16 kHz) and low-pass (0.5-2 kHz) noise and reverberation and SNRs and DRRs from -5 to + 20 dB were used. ITD dominated in quiet, anechoic conditions. ITD was downweighted and ILD upweighted with decreasing SNR for both noises. Only downweighting of ITD and upweighting of ILD were associated with decreasing DRR in wideband and low-pass reverberation, respectively. In general, listeners increased the relative weighting of ILD in more adverse listening conditions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".