Accuracy of Tempo Judgments in Disk Jockeys Compared to Musicians and Untrained Individuals
Bibliographic record
Abstract
Professional disk jockeys (DJs) are an under-studied population whose performance involves creating new musical experiences by combining existing musical materials with a high level of temporal precision. In contemporary electronic dance music, these materials have a stable tempo and are composed with the expectation for further transformation during performance by a DJ for the audience of dancers. Thus, a fundamental aspect of DJ performance is synchronizing the tempo and phase of multiple pieces of music, so that over seconds or even minutes, they may be layered and transitioned without disrupting the rhythmic pulse. This has been accomplished traditionally by manipulating the speed of individual music pieces "by ear," without additional technological synchronization aids. However, the cumulative effect of this repeated practice on auditory tempo perception has not yet been evaluated. Well-known phenomena of experience-dependent plasticity in other populations, such as musicians, prompts the question of whether such effects exist in DJs in their domain of expertise. This pilot study examined auditory judgments of tempo in 10 professional DJs with experience mixing by ear, compared to 7 percussionists, 12 melodic instrumental musicians, and 11 untrained controls. Participants heard metronome sequences between 80 and 160 beats per minute (BPM) and estimated the tempo. In their most-trained tempo range, 120-139 BPM, DJs were more accurate (lower absolute percent error) than untrained participants. Within the DJ group, 120-139 BPM exhibited greater accuracy than slower tempos of 80-99 or 100-119 BPM. DJs did not differ in accuracy compared to percussionists or melodic musicians on any BPM range. Percussionists were more accurate than controls for 100-119 and 120-139 BPM. The results affirm the experience-dependent skill of professional DJs in temporal perception, with comparable performance to conventionally trained percussionists and instrumental musicians. Additionally, the pattern of results suggests a tempo-specific aspect to this training effect that may be more pronounced in DJs than percussionists and musicians. As one of the first demonstrations of enhanced auditory perception in this unorthodox music expert population, this work opens the way to testing whether DJs also have enhanced rhythmic production abilities, and investigating the neural substrates of this skill compared to conventional musicians.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".