A Modeling Study of Music and Dance Rhythm Matching Using Time Series Analysis
Bibliographic record
Abstract
Rhythm matching of music and dance is an important research area in cross-modal analysis.In this paper, a music and dance rhythm matching algorithm based on time series analysis is proposed to extract the time series features of music and dance, and a genetic algorithm is used to determine the correspondence between music and dance movements to reflect the degree of correlation between changes in music and dance rhythm movements.In order to improve the matching and smoothing degree between the dance movement time series and the music time series, a constraint-based dynamic programming algorithm is introduced.The experimental results show that the model performs well in the matching degree and matching efficiency enhancement between dance movement time series and music time series, and its matching efficiency is 2-3 times of the traditional method.It shows high practicality in dance choreography and music matching, and can match any music clip with smooth and beautiful dance movements.The research in this paper provides new technical means for dance choreography and music matching, which will further optimize the transition harmony between music time series and dance movement time series.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".