Acoustic Software Defined Platform: A Versatile Sensing and General Benchmarking Platform
Bibliographic record
Abstract
Acoustic sensing has attracted significant attention recently, thanks to the pervasive availability of device support. However, adopting consumer-grade devices (e.g., smartphones) to deploy acoustic sensing applications faces the challenge of device/OS heterogeneity. Researchers have to pay tremendous efforts in tackling platform-dependent details even in simply accessing raw audio samples, thus losing focus on innovating sensing algorithms. To this end, this paper presents the first Acoustic Software Defined Platform (ASDP): a versatile sensing and general benchmarking platform. ASDP encompasses several customized acoustic modules running on a ubiquitous computing board, backed by a dedicated software framework. It is superior to commodity devices in controlling and reconfiguring physical layer settings, thus offering much better usability. The tailored software framework abstracts platform details and provides user-friendly interface for fast prototyping, while maintaining adequate programmability. To demonstrate the usefulness of ASDP, we showcase several relevant applications based on it. The promising outcomes make us believe that the release of our ASDP could greatly advance acoustic sensing research.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".