MapLooper: Live-looping of distributed gesture-to-sound mappings
Bibliographic record
Abstract
This paper presents the development of MapLooper: a live-looping system for gesture-to-sound mappings. We first reviewed loop-based Digital Musical Instruments (DMIs). We then developed a connectivity infrastructure for wireless embedded musical instruments with distributed mapping and synchronization. We evaluated our infrastructure in the context of the real-time constraints of music performance. We measured a round-trip latency of 4.81 ms when mapping signals at 100 Hz with embedded libmapper and an average inter-onset delay of 3.03 ms for synchronizing with Ableton Link. On top of this infrastructure, we developed MapLooper: a live-looping tool with 2 example musical applications: a harp synthesizer with SuperCollider and embedded source-filter synthesis with FAUST on ESP32. Our system is based on a novel approach to mapping, extrapolating from using FIR and IIR filters on gestural data to using delay-lines as part of the mapping of DMIs. Our system features rhythmic time quantization and a flexible loop manipulation system for creative musical exploration. We open-source all of our components.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".