A Knowledge-Based, Data-Driven Method For Action-Sound Mapping
Bibliographic record
Abstract
This paper presents a knowledge-based, data-driven method for using data describing action-sound couplings collected from a group of people to generate multiple complex mappings between the performance movements of a musician and sound synthesis. This is done by using a database of multimodal motion data collected from multiple subjects coupled with sound synthesis parameters. A series of sound stimuli is synthesised using the sound engine that will be used in performance. Multimodal motion data is collected by asking each participant to listen to each sound stimulus and move as if they were producing the sound using a musical instrument they are given. Multimodal data is recorded during each performance, and paired with the synthesis parameters used for generating the sound stimulus. The dataset created using this method is then used to build a topological representation of the performance movements of the subjects. This representation is then used to interactively generate training data for machine learning algorithms, and define mappings for real-time performance. To better illustrate each step of the procedure, we describe an implementation involving clarinet, motion capture, wearable sensor armbands, and waveguide synthesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".