An empirical comparison of three audio fingerprinting methods in music and feature-length film
Bibliographic record
Abstract
An empirical comparison of three audio fingerprinting methods in music and feature-length film is presented. Shazam, a commercially successful algorithm was chosen and against two vision-based algorithms, the original CMU algorithm, and also Google's Waveprint algorithm. The song or film of each of the queries corresponding to the respective dataset in turn is identified. The feature-length film dataset was transcoded and down- sampled from 48KHz to 16KHz mono-channel PCM with 256 kbps bitrates. The experiments were conducted on a machine with a single 3.0GHz Intel Xeon CPU with a 4MB cache and 16GB RAM. The F-measures on the two datasets show that optimizations for quality pay very high dividends on film audio, but not on music data. It is also found that the Shazam handily outperforms both vision- based algorithms, in both time and quality.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".