Pitch-based feature extraction for audio classification
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a new algorithm to discriminate between speech and non-speech audio segments. It is intended for security applications as well as talker location identification in audio conferencing systems, equipped with microphone arrays. The proposed method is based on splitting the audio segment into small frames and detecting the presence of pitch on each one of them. The ratio of frames with pitch detected to the total number of frames is defined as the pitch ratio and is used as the main feature to classify speech and non-speech segments. The performance of the proposed method is evaluated using a library of audio segments containing female and male speech, and non-speech segments such as computer fan noise, cocktail noise, footsteps, and traffic noise. It is shown that the proposed algorithm can achieve correct decision of 97% for the speech and 98% for non-speech segments, 0.5-seconds long.
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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