The Effects of Network Delay on Tempo in Musical Performance
Bibliographic record
Abstract
Internet-based collaborative networking applications, such as instant messaging, voice-over-IP telephony, and social networking, have displaced traditional communication services and redefined social interaction. The Internet has also transformed the music industry, revolutionizing the way music is distributed and marketed. Yet despite these two powerful trends, the intersection—where collaboration and music meet in online musicmaking—has remained merely a curiosity. Why? Artistically pleasing online audio collaboration requires network delay less than that encountered typically in the Internet. The bandwidth required for high-quality audio exceeds the bandwidth that is generally available on consumer-oriented broadband access (cable and digital subscriber line [DSL]) systems. The emergence of Web 2.0, broadly defined as Web-based communities such as social-networking sites that facilitate sharing of ideas among Web users, has been significant for many existing online communities. One such community, made up of real-time Web-based music collaborators using systems for networked musical performance (NMP), is in its infancy. An online NMP application lets musicians from across the globe play together over the Internet, as if they were together in a studio. With online music-making (either improvisatory or strictly notated), musicians can create ensembles without location bounds, searching for musicians around the world. The quality of the user’s experience is critical to the success of this Web application. However, because performing artists are highly sensitive to delay, network latency affects the quality of the user experience of online music-making. To achieve a good user experience the latency over the network has to be within reasonable bounds. If the delay is excessive, then the musicians will not be
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".