Follow the sound of my violin: Granger causality reflects information flow in sound
Bibliographic record
Abstract
Recent research into how musicians coordinate their expressive timing, phrasing, articulation, dynamics, and other stylistic characteristics during performances has highlighted the role of predictive processes, as musicians must anticipate how their partners will play in order to be together. Several studies have used information flow techniques such as Granger causality to show that upcoming movements of a musician can be predicted from immediate past movements of fellow musicians. Although musicians must move to play their instruments, a major goal of music making is to create a joint interpretation through the sounds they produce. Yet, information flow techniques have not been applied previously to examine the role that fellow musicians' sound output plays in these predictive processes and whether this changes as they learn to play together. In the present experiment, we asked professional violinists to play along with recordings of two folk pieces, each eight times in succession, and compared the amplitude envelopes of their performances with those of the recordings using Granger causality to measure information flow and cross-correlation to measure similarity and synchronization. In line with our hypotheses, our measure of information flow was higher from the recordings to the performances than vice versa, and decreased as the violinists became more familiar with the recordings over trials. This decline in information flow is consistent with a gradual shift from relying on auditory cues to predict the recording to relying on an internally-based (learned) model built through repetition. There was also evidence that violinists became more synchronized with the recordings over trials. These results shed light on the planning and learning processes involved in the aligning of expressive intentions in group music performance and lay the groundwork for the application of Granger causality to investigate information flow through sound in more complex musical interactions.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".