Feeling the Effort of Classical Musicians - A Pipeline from Electromyography to Smartphone Vibration for Live Music Performance
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents the MappEMG pipeline. The goal of this pipeline is to augment the traditional classical concert experience by giving listeners access, through the sense of touch, to an intimate and non-visible dimension of the musiciansâ bodily experience while performing. The live-stream pipeline produces vibrations based on muscle activity captured through surface electromyography (EMG). Therefore, MappEMG allows the audience to experience the performerâs muscle effort, an essential component of music performance which is typically unavailable to direct visual observation. The paper is divided in four sections. First, we overview related works on EMG, music performance, and vibrotactile feedback. We then present conceptual and methodological issues of capturing musiciansâ muscle effort related to their expressive intentions. We further explain the different components of the live-stream data pipeline: a python software named Biosiglive for data acquisition and processing, a Max/MSP patch for data post-processing and mapping, and a mobile application named hAPPtiks for real-time control of smartphonesâ vibration. Finally, we address the application of the pipeline in an actual music performance. Thanks to their modular structure, the tools presented could be used in different creative and biomedical contexts involving gestural control of haptic stimuli.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it