A Study of Multi-dimensional Melodic Similarity Model Based on Perceptual Analysis
Bibliographic record
Abstract
Perceived predominant melody of music is the most convenient and memorable description and can be used for content-based music retrieval. However, including human voice and multiple musical instruments playing together, it is difficult to extract a predominant contour of pitches directly from MP3 music recordings. In order to build a similarity music melody model, several audio files, whose melody is perceptually similar, are collected in our experiment. Many features are extracted and a melodic similarity model is defined through analyzing each feature and combinations of them. The melody model is evaluated based on classification results of six categories of Chinese folk music using Support Vector Machine. The experiment results show that 36-dimensional Constant Q Transform (CQT) feature can represent the melody of audio music pieces accurately. Further more, the classification results for audio data with similar melody are good enough to be used in audio music classification or segmentation and subsequently are very helpful in music information retrieval system.
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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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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".