Accuracies in Algorithmic Predictors of Musical Emotion
Bibliographic record
Abstract
Music information retrieval (MIR) is a growing area of study that aims to algorithmically analyse musical features from audio recordings. Curiously, despite increasing use of MIR algorithms, few studies have examined their accuracy. Here, we evaluate the accuracy of two functions within MIRToolbox—a software library widely used in the field of music cognition for automated musical analyses. We focus on two key musical features—modality (specific note groupings that contribute to the emotional aspect of music) and attack rate (a global measure of timing information). To compare algorithmic estimates of modality and timing against known information about these features, we used “ground truth” data from a widely, historically important set of pieces. Specifically, we analysed (a) modality and (b) attack rate in four complete performances of Frederic Chopin’s Préludes (Chopin, 1839). Timing analyses revealed accurate predictions for slow pieces, but reduced accuracy for fast pieces. Mode analyses were generally accurate, but consistency varied between performers (79% to 88%). For each performer, the algorithm incorrectly predicted mode in at least three, and at most five, excerpts out of 24. This preliminary exploration of popular MIR algorithms offers valuable insights that shed light on the limitations of widely-used tools. Addressing these limitations will lay the foundation for future research endeavours that aim to delve deeper into the application of these algorithms across a wide range of musical works and genres.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".