Shake, rattle, and roll : Getting immersed in multisensory, interactive music via broadband networks
Bibliographic record
Abstract
Broadband Internet (transmission rates more than a gigabit per second) enables bidirectional real-time transmission of multiple streams of audio, video, and motion data with latency dependent on distance plus network and processing delays. In this article we describe a new immersive multisensory environment recently constructed at McGill University, designed for network-based communication for music performance coordinated between remote sites, potentially over great distance. The system's architecture allows participants to experience the music with greatly enhanced presence through the use of multiple sensors and effectors and high-resolution multimodal transmission channels. Up to 24 channels of audio, digital video, and four channels of vibration can be sent and received over the network simultaneously, allowing a number of diverse applications such as remote music teaching, student auditions, jam sessions and concerts, recording sessions, and postproduction for remotely-captured live events. The technical and operational challenges of this undertaking are described, as well as potential future applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".