Comparison of machine and human recognition of isolated instrument tones
Bibliographic record
Abstract
This paper describes three different machine recognition experiments and a recently conducted human experiment in order to compare the abilities of machines and humans to recognize isolated instrument tones. The computer recognition software is based on the Lazy Learning Machine, which is an exemplar-based learning system using a k-nearest neighbor (k-NN) classifier with a genetic algorithm to find the optimal set of weights for the features to improve its performance. The performance of the software was progressively improved by adding more features. These include centroid and other higher order moments, such as skewness and kurtosis, the velocities of moments, spectral irregularity, tristimulus, and time–domain envelope shape. Also, realtime recognition is now possible by using Miller Pucketts PD, a realtime software synthesis system, and his fiddle∼ object. The training data was taken from the McGill Master Samples. The human experiment involved eighty-eight conservatory students. Although the average human scores are similar to the machine scores, the best human subjects far exceeded the capabilities of the machine. The excellent performance of the humans in this experiment presents new challenges for timbre-recognition computer 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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".