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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".