An initiative for testability and comparability of binaural models
Bibliographic record
Abstract
A framework aimed at improving the testability and comparability of binaural models will be presented. The framework consists of two key elements: (1) a repository of testing software that evaluates the models against published data and (2) a model repository. While the framework is also intended for physiological data, the planned initial contribution will be psychoacoustical data together with their psychoacoustical testing protocols, as well as existing binaural models from available auditory toolboxes. Researchers will be invited to provide their established as well as newly developed models in whatever programming language they prefer, given the models are compatibility with the proposed interface to the testing software. This entails that the models act as artificial observers, testable with exactly the same procedure as the human subjects. A simple communication protocol based on wav and txt-files is proposed because these are supported by every programming environment, and are able connect models and testing software of any programming language. Examples will illustrate the principle of testing models with unaltered signal processing stages on various seminal data sets such as tone detection in so-called double-delayed masking noise, or lateralization of ¾-period delayed noise and sounds with temporally asymmetric envelopes.
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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 | 0.162 | 0.296 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.012 | 0.021 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".