Modeling Musical Mood From Audio Features, Affect and Listening Context on an In-situ Dataset
Bibliographic record
Abstract
Musical mood is the emotion that a piece of music expresses. When musical mood is used in music recommenders (i.e., systems that recommend music a listener is likely to enjoy), salient suggestions that match a user’s expectations are made. The musical mood of a track can be modeled solely from audio features of the music; however, these models have been derived from musical data sets of a single genre and labeled in a laboratory setting. Applying these models to data sets that reflect a user’s actual listening habits may not work well, and as a result, music recommenders based on these models may fail. Using a smartphone-based experience-sampling application that we developed for the Android platform, we collected a music listening data set gathered in-situ during a user’s daily life. Analyses of our data set showed that real-life listening experiences differ from data sets previously used in modeling musical mood. Our data set is a heterogeneous set of songs, artists, and genres. The reasons for listening and the context within which listening occurs vary across individuals and for a single user. We then created the first model of musical mood using in-situ, real-life data. We showed that while audio features, song lyrics and socially-created tags can be used to successfully model musical mood with classification accuracies greater than chance, adding contextual information such as the listener’s affective state and or listening context can improve classification accuracies. We successfully classified musical arousal in a 2-class model with a classification accuracy of 67% and musical valence with an accuracy of 75%. Finally, we discuss ways in which the classification accuracies can be improved, and the applications that result from our models.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".