Challenges with real-world smartwatch based audio monitoring
Bibliographic record
Abstract
Audio data from a microphone can be a rich source of information. The speech and audio processing community has explored using audio data to detect emotion, depression, Alzheimer's disease and even children's age, weight and height. The mobile community has looked at using smartphone based audio to detect coughing and other respiratory sounds and help predict students' GPA. However, audio data from these studies tends to be collected in more controlled environments using well placed, high quality microphones or from phone calls. Applying these kinds of analyses to continuous and in-the-wild audio could have tremendous applications, particularly in the context of health monitoring. As part of a health monitoring study, we use smartwatches to collect in-the-wild audio from real patients. In this paper we characterize the quality of the audio data we collected. Our findings include that the smartwatch based audio is good enough to discern speech and respiratory sounds. However, extracting these sounds is difficult because of the wide variety of noise in the signal and current tools perform poorly at dealing with this noise. We also find that the quality of the microphone allows annotators to differentiate the source of speech and coughing, which adds another level of complexity to analyzing this audio.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".