A general-purpose pipeline to interface the Tympan hardware with an external computer
Bibliographic record
Abstract
Some audio applications require resource-heavy algorithms which cannot be run on real-time digital signal processors such as the Tympan hardware directly due to memory and processing constraints. These algorithms can, however, run on an external computer (PC), and their outcomes can be relayed back to the Tympan where necessary adjustments can be made in the audio processing elements of the underlying hardware. The proposed pipeline includes a 4-channel audio input, the algorithm running on the PC, the Tympan hardware, and a 4-channel output. The Tympan hardware acquires input audio from the Tympan’s onboard and/or external microphones with the I2S protocol. The input data are transmitted to the algorithm running on the external PC via USB/Serial communication by exposing the Tympan as a soundcard interface. The output of the PC algorithm is then sent back to the Tympan, through serial communication, so that the underlying audio processing parameters are adjusted. A possible use case is also discussed: a detection algorithm runs on the PC and serial commands are sent back to Tympan to alter the audio outputs, e.g., by playing masking noise. The pipeline is built such that each element can be used independently, abstracting the interconnection of the pipeline elements.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".