A Comparison of Approaches to Timbre Descriptors in Music Information Retrieval and Music Psychology
Bibliographic record
Abstract
A curious divide characterizes the usage of audio descriptors for timbre research in music information research (MIR) and music psychology. While MIR uses a multitude of audio descriptors for tasks such as automatic instrument classification, only a highly constrained set is used to describe the physical correlates of timbre perception in parts of music psychology. We argue that this gap is not coincidental and results from the differences in the two fields’ methodologies, their epistemic groundwork, and research goals. This paper lays out perspectives on the emergence of the divide and reviews studies in both fields with regards to divergences in research methods and goals. We discuss new representations for spectro-temporal modulations in MIR and psychology, and compare approaches to spectral envelope description in depth. Finally, we will propose that the interdisciplinary discourse on the computational modelling of music requires negotiations about the roles of scientific evaluation criteria.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".