PAMBOX: A Python auditory modeling toolbox
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Poster presented at EuroScipy 2014. Toolboxes for modeling auditory perception have a surprisingly long history, starting with the Auditory Toolbox, first written by Malcom Slaney for Mathematica, in 1993, and then ported to Matlab in 1998. Here we present the Python Auditory Modeling Toolbox (PAMBOX), an open-source Python package for auditory modeling. The goal of the toolbox is to provide a collection of components that can be easily combined and extended to solve auditory modeling problems. PAMBOX contains code for modeling cochlear filtering, envelope extraction, as well as modulation processing. The toolbox also includes speech intelligibility models. These models are commonly used to predict how well speech is understood in a given situation, such as in the presence of noise or reverberation. The intelligibility models use a simple and consistent "predict" API, inspired by scikit-learn's "fit and predict" API. This simplifies comparisons across models. PAMBOX also includes a framework for performing intelligibility experiments compatible with IPython.parallel. Models that are not original to PAMBOX are validated against their original implementations, where available. PAMBOX is based on NumPy, SciPy, and Pandas. It is distributed under the Modified BSD License.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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 it