Modeling auditory localization in the low-frequency range
Bibliographic record
Abstract
Binaural recordings were made for subwoofer reproduction of octave-band noise bursts at 31.5-Hz, 63-Hz and 125-Hz center frequencies, and these low-frequency responses were analyzed using a binaural model simulating human perception. As expected, the interaural level differences remained nearly constant for different sound source positions within this low-frequency range. On the basis of interaural time differences, however, the model was able to predict the left/right position of the sound source on the interaural axis. In order to visualize the cross-correlation peak at low frequencies in the ITD range from −1.5 ms to +1.5 ms, the cross-correlation functions were decompressed by taking them to the power of 40. At these low-frequencies, the range of phase difference does not vary much with different sound positions although the ITDs are on the same order as for higher frequencies (≊−1.0 ms to 1.0 ms), but the human ability to resolve very small phase differences already has been shown in previous investigations. The predictions of the model simulation were verified in a listening test. The repetition of the experiment in a second more reverberant space showed similar reductions in performance for both the human listeners and the model. [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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".