Volume-Independent Music Matching by Frequency Spectrum Comparison
Bibliographic record
Abstract
Currently, there are applications such as Shazam that provides music matching. However, a limitation is that the same piece performed by the same musician cannot be identified if it is not the same recording. This is because Shazam matches the variation in volume, not the frequencies of the sound. This research attempts to match music the way humans understand it: by the frequency spectrum of music, not the volume variation. We pre-computed the frequency spectrums of the music, then took the unknown piece and tried to match its frequency spectrum against every segment. We did so by sliding the window by 0.1 seconds and calculating the error by subtracting the normalized arrays and taking the sum of absolute differences. The segment that showed the least error was considered the candidate for the match. Matching simple pieces such as single-note pieces was successful, but complex pieces such as symphonies were not successful; that is, the algorithm couldn’t produce low error value in any of the music in the database. We suspect that it has to do with having “too many notes,” i.e., mismatches in the higher harmonics added up to significant amount of errors, which swamps the calculations.
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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 | 0.006 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".