Training Surrogate Sensors in Musical Gesture Acquisition Systems
Bibliographic record
Abstract
Capturing the gestures of music performers is a common task in interactive electroacoustic music. The captured gestures can be mapped to sounds, synthesis algorithms, visuals, etc., or used for music transcription. Two of the most common approaches for acquiring musical gestures are: 1) “hyper-instruments” which are “traditional” musical instruments enhanced with sensors for directly detecting the gestures and 2) “indirect acquisition” in which the only sensor is a microphone capturing the audio signal. Hyper-instruments require invasive modification of existing instruments which is frequently undesirable. However, they provide relatively straightforward and reliable sensor measurements. On the other hand, indirect acquisition approaches typically require sophisticated signal processing and possibly machine learning algorithms in order to extract the relevant information from the audio signal. The idea of using direct sensor(s) to train a machine learning model for indirect acquisition is proposed in this paper. The resulting trained “surrogate” sensor can then be used in place of the original direct invasive sensor(s) that were used for training. That way, the instrument can be used unmodified in performance while still providing the gesture information that a hyper-instrument would provide. In addition, using this approach, large amounts of training data can be collected with minimum effort. Experimental results supporting this idea are provided in two detection contexts: 1) strike position on a drum surface and 2) strum direction on a sitar.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".