Efficient spectrogram-based binary image feature for audio copy detection
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents the latest improvements on our Spectro system that detects transformed duplicate audio content. We propose a new binary image feature derived from a spectrogram matrix by using a threshold based on the average of the spectral values. We quantize this binary image by applying a tile of fixed size and computing the sum of each small square in the tile. Fingerprints of each binary image encode the positions of the selected tiles. Evaluation on TRECVID 2010 CBCD data shows that this new feature improves significantly the Spectro system for transformations that add irrelevant speech to the audio. Compared to a state-of-the-art audio fingerprinting system, the proposed method reduces the minimal Normalized Detection Cost Rate (min NDCR) by 33%, improves localization accuracy by 28% and results in 40% fewer missed queries.
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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 | 0.000 |
| 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 it