Encountering Cover Versions of Songs Derived from Personal Music-Listening History Data: a Design and Field Trial of Musée in Homes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it