Beatmatching in DJing : An analysis of temporal coordination using electroencephalogram (EEG), motion capture, and audio analysis
Bibliographic record
Abstract
DJing is a sophisticated musical skill involving complex temporal perception and active manipulation of multiple, often polyrhythmic patterns simultaneously. In the process of beatmatching, DJs synchronise two different records that are out of phase or playing at different tempo. This is an embodied, dynamic activity relying on a coordinated system of processes in the body, the brain, the turntables and the sonic patterns in the music. The aim of this work is to explore the interactions occurring between these dynamic processes and to compare them over a set of behaviours commonly exhibited during beatmatching. We collected 121 audio, EEG, and motion capture data from 28 DJs. The task was to beatmatch two tracks initially playing at different phases (shifted one sixth of a bar) or tempi (130 vs 135 bpm) using digital turntables with the automatic sync function turned off. We then extracted a phase representation for each dataset to study synchronisation between the two tracks and body movement. In initial analysis of the audio, we extracted the dynamic changes to the tracks over time, identifying windows of distinct behaviour. In the movement data, we identified associated windows of rhythmic movement locked to the beat. Together, these analyses provide a clear picture of the behavioural modes. Using these results, we then compared functional neural connectivity across different behavioural modes including slip cueing, nudging the platter, adjusting the pitch control, and monitoring the resulting mix. In this poster, we will present preliminary EEG analyses relating the combined audio and motor modes to features of the functional networks. This research has potential to improve understanding of the role of movement, brain states, and their interactions in coordinating the beatmatching process. More broadly, this work supports an embodied approach to studying how humans coordinate complex rhythmic behaviours to complex rhythmic stimuli in their environments.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".