Timbral cues for learning to generalize musical instrument identity across pitch register
Bibliographic record
Abstract
Timbre provides an important cue to identify musical instruments. Many timbral attributes covary with other parameters like pitch. This study explores listeners' ability to construct categories of instrumental sound sources from sounds that vary in pitch. Nonmusicians identified 11 instruments from the woodwind, brass, percussion, and plucked and bowed string families. In experiment 1, they were trained to identify instruments playing a pitch of C4, and in experiments 2 and 3, they were trained with a five-tone sequence (F#3-F#4), exposing them to the way timbre varies with pitch. Participants were required to reach a threshold of 75% correct identification in training. In the testing phase, successful listeners heard single tones (experiments 1 and 2) or three-tone sequences from (A3-D#4) (experiment 3) across each instrument's full pitch range to test their ability to generalize identification from the learned sound(s). Identification generalization over pitch varies a great deal across instruments. No significant differences were found between single-pitch and multi-pitch training or testing conditions. Identification rates can be predicted moderately well by spectrograms or modulation spectra. These results suggest that listeners use the most relevant acoustical invariance to identify musical instrument sounds, also using previous experience with the tested instruments.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".