A meta-analysis of acoustic correlates of timbre dimensions
Bibliographic record
Abstract
A meta-analysis of ten published timbre spaces was conducted using multidimensional scaling analyses (CLASCAL) of dissimilarity ratings on recorded, resynthesized, or synthesized musical instrument tones. A set of signal descriptors derived from the tones was drawn from a large set developed at IRCAM, including parameters derived from the long-term amplitude spectrum (slope, centroid, spread, deviation, skewness, kurtosis), from the waveform and amplitude envelope (attack time, fluctuation, roughness), and from variations in the short-term amplitude spectrum (flux). Relations among all descriptors across the 128 sounds were used to determine families of related descriptors and to reduce the number of descriptors tested as predictors. Subsequently multiple correlations between descriptors and the positions of timbres along perceptual dimensions determined by the CLASCAL analyses were computed. The aim was (1) to select the subset of acoustic descriptors (or their linear combinations) that provided the most generalizable prediction of timbral relations and (2) to provide a signal-based model of timbral description for musical instrument tones. Four primary classes of descriptors emerge: spectral centroid, spectral spread, spectral deviation, and temporal envelope (effective duration/attack time). [Work supported by CRC, CFI, NSERC, CUIDADO European Project.]
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 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 | 0.034 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.024 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".