A Bayesian computational basis for auditory selective attention using head rotation and the interaural time-difference cue
Bibliographic record
Abstract
The process of resolving mixtures of several sounds into their separate individual streams is known as auditory scene analysis and it remains a challenging task for computational systems. It is well-known that animals use binaural differences in arrival time and intensity at the two ears to find the arrival angle of sounds in the azimuthal plane, and this localization function has sometimes been considered sufficient to enable the un-mixing of complex scenes. However, the ability of such systems to resolve distinct sound sources in both space and frequency remains limited. The neural computations for detecting interaural time difference (ITD) have been well studied and have served as the inspiration for computational auditory scene analysis systems, however a crucial limitation of ITD models is that they produce ambiguous or "phantom" images in the scene. This has been thought to limit their usefulness at frequencies above about 1khz in humans. We present a simple Bayesian model and an implementation on a robot that uses ITD information recursively. The model makes use of head rotations to show that ITD information is sufficient to unambiguously resolve sound sources in both space and frequency. Contrary to commonly held assumptions about sound localization, we show that the ITD cue used with high-frequency sound can provide accurate and unambiguous localization and resolution of competing sounds. Our findings suggest that an "active hearing" approach could be useful in robotic systems that operate in natural, noisy settings. We also suggest that neurophysiological models of sound localization in animals could benefit from revision to include the influence of top-down memory and sensorimotor integration across head rotations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".