Connecting Free Improvisation Performance and Drumming Gestures Through Digital Wearables
Bibliographic record
Abstract
High-level improvising musicians master idiosyncratic gesture vocabularies that allow them to express themselves in unique ways. The full use of such vocabularies is nevertheless challenged when improvisers incorporate electronics in their performances. To control electronic sounds and effects, they typically use commercial interfaces whose physicality is likely to limit their freedom of movement. Based on Jim Black's descriptions of his ideal digital musical instrument, embodied improvisation gestures, and stage performance constraints, we develop the concept of a modular wearable MIDI interface to closely meet the needs of professional improvisers, rather than proposing a new generic instrument that would require substantial practice to adapt improvisational techniques already acquired. Our research draws upon different bodies of knowledge, from theoretical principles on collaboration and embodiment to wearable interface design, in order to create a digital vest called Track It, Zip It (TIZI) that features two innovative on-body sensors. Allowing for sound control, these sensors are seamlessly integrated with Black's improvisational gesture vocabulary. We then detail the design process of three TIZI prototypes structured by the outcomes of a performance test with Black, a public performance by a novice improviser during the 2017 International Guthman Musical Instrument Competition, and measurements of sensor responses. After commenting on the strengths and weaknesses of the final TIZI prototype, we discuss how our interdisciplinary and collective process involving a world-class improviser at the very center of the design process can provide recommendations to designers who wish to create interfaces better adapted to high-level performers. Finally, we present our goals for the future creation of a wireless version of the vest for a female body based on Diana Policarpo's artistic vision.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".