Adaptive Feature Selection for Speech / Music Classification
Bibliographic record
Abstract
In this paper, we propose a new system for classifying audio segments as speech or music. The proposed system improves classification accuracy, particularly in low signal-to-noise ratio (SNR) environments. The system selects the features with the highest classification accuracy that corresponds to the SNR value. The value of this features are compared to certain thresholds, which are also adapted to the SNR. Multi-expert method of combining the features to improve classification accuracy is implemented. A new feature, termed the variance of low-band energy ratio, is also introduced. This feature produces large improvements in classification accuracy at low SNR. Performance of the proposed system is evaluated for different SNR using a library of speech and music audio segments. Using one-second segments it is shown that the proposed system can enhance the classification accuracy by 22% at SNR = -15 dB, and obtain classification accuracy of 90.3% at SNR = 0 dB.
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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 | 0.000 | 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 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".