The Study of Learning System for Infant Cry Classification Using Discrete Wavelet Transform and Extreme Machine Learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The learning system of infant cry is presented. This system consists of characteristics attraction technique and classification technique. The characteristics attraction of infant cry are based on Discrete Wavelet Transform (DWT) methods. Whilst the sound classification of coefficients characteristics uses Single Layer Neural Feed Forward (SLNF) as an Extreme Learning Machine (ELM). The Dunstan Baby Language (DBL) is the sound database for the proposed system. The sound database was collected from infants between birth and 6 months of age. Where the baby language groups are categorized into 5 types: "Eh", "Eairh", "Neh", "Heh" and "Owh", respectively. The accuracy of sound classification was designated at the number of hidden nodes of 10 – 50 with a training and testing ratio of 70/30. The suitable results are based on the number of epochs, accuracy and performances. The results show that the average accuracy of all discrete wavelet functions on the baby language are over 80%. The average performance of Sym2 is suitable for all baby language groups. Moreover, the average number of epochs of Bior3.1 is suitable for all baby language groups.
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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 | 0.003 | 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 | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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