Encountering Cover Versions of Songs Derived from Personal Music-Listening History Data: a Design and Field Trial of Musée in Homes
Bibliographic record
Abstract
Abstract We designed and implemented Musée to capture the novel experience of interpreting cover versions of music, which contain both familiar and unfamiliar musical components and are curated based on the user’s music-streaming history data. Musée is a tangible music player that enables users to explore and listen to professional or amateur covers of songs (via YouTube) in two categories: covers of songs from users’ most-liked artists and covers of users’ most-played songs. To investigate its potential value in situ, we conducted field trials of Musée in four households for 1 month. Findings showed that unfamiliar musical elements in cover music provided a sense of ‘freshness’ to past songs and helped the listener appreciate over-consumed music in new ways. In addition, restricting detailed information about cover songs that were playing helped users focus on the sound, thus priming them to infer and reflect on the original song and their memories associated with it. Our findings point to new insights for the design of interfaces that use historical personal data to expand users’ experience beyond solely revisiting prior tastes.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".