The Wiikembe — performer designed lamellophone hyperinstrument for idiomatic musical-DSP interaction
Bibliographic record
Abstract
The Wiikembe is an augmented Likembe from Zaire, believed to be 100+ years old. A Wiimote affords 3D gesture sensing for musical-HCI. An Arduino interface offers explicit control over DSP functions. Puredata (Pd) scales, converts, and routes control data into Ableton Live. A contact mic is used to acquire a direct audio signal from the Likembe. The audio inputs into a conventional computer audio interface and routed into Live which handles event sequencing, DSP, and audio bussing. The result is a compact and intuitive, robust lamellophone hyperinstrument. The Wiikembe extends the sonic possibilities of the acoustic Likembe without compromising traditional sound production methods or performance techniques. We chose specific sensors and their placement based on constraints regarding the instrument's construction, playing techniques, the author's idiosyncratic compositional approach and sound design requirements. The Wiikembe leverages and combines inherent performance gestures with analogous embedded gestural sensing to achieve unprecedented intimate musical-DSP interaction. Specific gesture recognition techniques and mapping strategies have been standardized using easily sourced and implementable, low-cost components. This work is in efforts to establish an implementable pitched-percussion hyperinstrument framework for experimentation and pedagogy with minimal engineering requirements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.026 | 0.006 |
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".