Gaussian Mixture Modeling of Short-Time Fourier Transform Features for Audio Fingerprinting
Bibliographic record
Abstract
In audio fingerprinting, an audio clip must be recognized by matching an extracted fingerprint to a database of previously computed fingerprints. The fingerprints should reduce the dimensionality of the input significantly, provide discrimination among different audio clips, and, at the same time, be invariant to distorted versions of the same audio clip. In this paper, we design fingerprints addressing the above issues by modeling an audio clip by Gaussian mixture models (GMM). We evaluate the performance of many easy-to-compute short-time Fourier transform features, such as Shannon entropy, Renyi entropy, spectral centroid, spectral bandwidth, spectral flatness measure, spectral crest factor, and Mel-frequency cepstral coefficients in modeling audio clips using GMM for fingerprinting. We test the robustness of the fingerprints under a large number of distortions. To make the system robust, we use some of the distorted versions of the audio for training. However, we show that the audio fingerprints modeled using GMM are not only robust to the distortions used in training but also to distortions not used in training. Among the features tested, spectral centroid performs best with an identification rate of 99.2% at a false positive rate of 10-4. All of the features give an identification rate of more than 90% at a false positive rate of 10-3
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".