A Novel Three Stage Framework for Person Identification From Audio Aesthetic
Bibliographic record
Abstract
Social behavioral biometrics investigates social interactions to determine a person’s identity. Within the discipline of social behavioral biometrics, recognition of individuals based on their aesthetic preferences is an emerging direction of research. Human aesthetic is a soft, behavioral biometric trait that refers to a person’s attitudes towards a particular subject material. Recent developments in aesthetic-based biometric systems have proven that an individual’s visual and audio aesthetic preferences hold considerable distinctive features. This paper introduces a novel three-stage audio-aesthetic system that can uniquely identify a user from the set of their favorite songs. The system utilizes Residual Network (ResNet) for high-level feature extraction. A hybrid meta-heuristic feature selection algorithm based on Cuckoo Search and Whale Optimization is proposed for feature extraction optimization, which results in the low-dimensional feature set. The selected subset of features is fed into the XGBoost classifier to establish a person’s identity. The proposed method outperformed the handcrafted feature-based method by achieving 99.54% accuracy on a proprietary dataset (Free Music Archive) and 99.79% accuracy on a publicly available dataset (Million Playlists Dataset).
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".