Bibliographic record
Abstract
We present SoundCraft, a smartwatch prototype embedded with a microphone array, that localizes angularly, in azimuth and elevation, acoustic signatures: non-vocal acoustics that are produced using our hands. Acoustic signatures are common in our daily lives, such as when snapping or rubbing our fingers, tapping on objects or even when using an auxiliary object to generate the sound. We demonstrate that we can capture and leverage the spatial location of such naturally occurring acoustics using our prototype. We describe our algorithm, which we adopt from the MUltiple SIgnal Classification (MUSIC) technique [31], that enables robust localization and classification of the acoustics when the microphones are required to be placed at close proximity. SoundCraft enables a rich set of spatial interaction techniques, including quick access to smartwatch content, rapid command invocation, in-situ sketching, and also multi-user around device interaction. Via a series of user studies, we validate SoundCraft's localization and classification capabilities in non-noisy and noisy environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.042 |
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".