Narrowcasting and Multipresence for Music Auditioning and Conferencing in Social Cyberworlds
Bibliographic record
Abstract
We describe a musical cyberworld, Folkways in Wonderland, in which avatarrepresented users can find and listen to selections from the Smithsonian Folkways world music collection. When audition is disturbed by cacophony of nearby tracks or avatar conversations, one’s soundscape can be refined since the system supports narrowcasting, a technique which allows information streams to be filtered. Our system supports two different kinds of sound sources: musical selections and avatar conversation (voice-chat). Narrowcasting for music enables aesthetic focus; narrowcasting for talk enables cognitive focus. The former is required for dense presentation of musical sound, the latter for virtual worlds in which many avatars are expected to be able to interact. An active listener can fork self-identified avatars using a novel multipresence technique, locating representatives at locations of interest, each clone capturing respective soundscapes, controlled using narrowcasting functions {self, non-self} × {select (solo), mute, deafen, attend}. Likewise one can participate in a conference and at the same time join a global tour of music. Our music browser is architected to use MX: IEEE 1599, a comprehensive, multilayered, music description standard. Using our cyberworld as a virtual laboratory, we evaluated the effectiveness of narrowcasting when auditioning music and conferencing. Experimental results suggest that narrowcasting and multipresence techniques are useful for collaborative music exploration and improve user experience. We also got positive feedback from the participants regarding narrowcasting representations, variously based on colors, symbols, and icons.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".