An Evaluation of the role of Mapping in Skill Acquisition on Digital Musical Instruments
Bibliographic record
Abstract
Digital Music Instruments (DMIs) have become popular both in research laboratories and among performers of experimental music scenes. The concept of DMIs is very attractive, however few instruments make it beyond a laboratory environment. There is an apparent lack of historical and musical background enjoyed by acoustic instruments as well as a lack of pedagogy used to evaluate the effectiveness of DMIs and DMI players. While initial evaluation methods can be borrowed from the field of Human Computer Interaction (HCI), these methods need to be extended to evaluate the DMIs' integrated layers: the physical interface, the sound synthesizer and the abstract mapping block. This research was designed around the evaluation of a novel DMI called the "Ballagumi" with two main objectives: firstly determine the extent to which it is possible to obtain skills on the Ballagumi in a musical context (using musical compositions as tasks along with an improvisation setting) and secondly to determine how much the design of the mapping block affects the acquisition of skill on the instrument. For this purpose, two studies were conducted, a preliminary study that focused on discovering gestures on the Ballagumi and obtaining an initial perspective of the instrument features (e.g. latency), and a second study where professional musicians experienced in electronic music controllers were invited to play musical tasks and also improvise on the instrument with both mappings. The study results revealed interesting insights on the instrument itself, namely that the physical interface allowed for much of the learning in the interaction as it is built with material that have passive haptic feedback. The results also showed that of the two designed mappings, the simpler choice (a direct energy input mapping) was preferred for improvisation by participants. The choice of mapping resulted in very different impressions of the performance even though the instrument and the sound synthesis layer remained constant. Manipulating the instrument on an intuitive level was made easier by a direct energy input mapping since the physical interfaced required energy input and made use of both hands. The choice of mapping also affected the extent to which participants enjoyed playing the Ballagumi. The results from these two studies can be used to further improve the Ballagumi's sound and physical attributes to have it be used by more musicians. They also not only demonstrate the importance of the mapping block in acquiring skills to the point of improvising on the DMI (confirming in this regard past literature on the subject) but highlight the importance of accounting for the existing feedback before adding levels of complexity to the mapping.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".