SnapTunes: An Introductory Cross-device Application for Collaborative Music Production
Bibliographic record
Abstract
Music creation has shifted from a collaborative, social experience to an individual and technical process through the use of digital audio workstations (DAWs), which often have steep learning curves that deter novice users. To address this, many new music creation applications prioritize social play through collaboration and exploration. However, tools that combine the structured concepts of traditional music production with social play remain relatively unexplored. This paper introduces SnapTunes, a tablet-based application where users individually create music tracks and connect their devices to form a combined composition. SnapTunes allows users to spatially arrange their devices, changing how the individual tracks are layered and sequenced. We playtested a Wizard-of-Oz-style prototype of SnapTunes in a workshop with 15 university students to gather insight on usability and social engagement. Findings demonstrate that SnapTunes was intuitive, enjoyable and created opportunities for playful collaboration. Usability feedback and directions for future development are discussed.
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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 | 0.001 |
| 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) | 0.000 | 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".