Unsupervised analysis of similarities between musicians and musical genres using spectrograms
Bibliographic record
Abstract
Since the early days of the information era, digital music has been becoming one of the most consumed types of media, introducingthe need for content-based tools that can search, browse, and retrieve music. Here we describe a method that canquantify similarities between musical genres in an unsupervised fashion, and computes networks of similarities between differentmusicians or musical styles. The method works by converting each song to its 2D spectrogram, and then extracting a largeset of 2883 2D numerical content descriptors. The descriptors are weighted by their informativeness, and then the similaritiesbetween the musical styles are measured using the weighted distances between the musical pieces of each pair of musicians orgenres. The similarities between all pairs provide a similarity matrix, which is visualized by a phylogeny. Experiments using23 well known musicians representing seven musical genres show that the algorithm was able to separate the artists into groupsthat are in agreement with their respective musical genres. The analysis was done in an unsupervised fashion, and without anyhuman definition or annotation of the musical styles.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".