Attentive Headphones: Augmenting Conversational Attention with a Real World TiVo ®
Bibliographic record
Abstract
Computer users in public transportation, coffee shop or cubicle farm environments require sociable ways to filter out noise generated by other people. Current use of noisecanceling headsets is detrimental to social interaction because these headsets do not provide context-sensitive filtering techniques. Headsets also provide little in terms of services that allow users to augment their attentive capabilities, for example, by allowing them to pause or fastforward conversations. We addressed such issues in our design of Attentive Headphones, a noise-cancelling headset sensitive to nonverbal conversational cues such as eye gaze. The headset uses eye contact sensors to detect when other people are looking at the wearer. Upon detecting eye gaze, the headset automatically turns off noise-cancellation, allowing users to attend to a request for attention. The headset also supports the execution of tasks that are parallel to conversational activity, by allowing buffering and fastforwarding of conversational speech. This feature also allows users to listen to multiple conversations at once.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".