Learning Indirect Acquisition of Instrumental Gestures using Direct Sensors
Bibliographic record
Abstract
Sensing instrumental gestures is a common task in interactive electroacoustic music performances. The sensed gestures can then be mapped to sounds, synthesis algorithms, visuals etc. Two of the most common approaches for acquiring these gestures are: 1) Hybrid instruments which are "traditional" musical instruments enhanced with sensors that directly detect gestures 2) Indirect acquisition in which the only measurement is the acoustic signal and signal processing techniques are used to acquire the gestures. Hybrid instruments require modification of existing instruments which is frequently undesirable. However they provide relatively straightforward and reliable measuring capability. 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 signals. In this paper the idea of using direct sensors to train a machine learning model for indirect acquisition is explored. This approach has some nice advantages, mainly: 1) large amounts of training data can be collected with minimum effort 2) once the indirect acquisition system is trained no sensors or modifications to the playing instrument are required. Case studies described in paper include 1) strike position on a snare drum 2) strum direction on a sitar
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".