Using Audio Loops for Instrument Family Recognition in Machine Learning Tasks
Bibliographic record
Abstract
This paper introduces an instrument recognition approach with the aid of audio loops. The aim is to show a basic instrument recognition recipe for music technology researchers by investigating whether the DAW-based audio loops can be an alternative to researched-based available libraries such as McGill University master samples, UIOWA samples, IRMAS audio libraries. For that purpose, audio loops from Apple Jam Pack were preferred to create instrument classes (Families). The loops were arranged according to their related instrument classes. The class names are Bass, Drums and Percussions, Guitars, Keyboards, Strings, Synthesizers, and Winds. After the extraction of temporal and spectral audio features from those classes, a 5736x105 dimensional dataset emerged. Then this dataset was examined with 19 different supervised machine learning algorithms. The SVM Cubic classification algorithm provided the best accuracy (90.2%). The result shows that the audio loops with mid-term feature extraction can be used for instrument recognition tasks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".