A robust and accurate cross-correlation-based fundamental frequency (F/sub 0/) determination method for the improved analysis of infant cries
Bibliographic record
Abstract
The extraction and analysis of the fundamental frequency (F/sub 0/) of infant vocalizations has been the focus of a number of efforts over the past thirty years. It is thought that this parameter is an important information channel from which information regarding the state of an infant can be determined. To date, research groups working to extract the vocal fundamental frequency of infant cries have been limited in the resolution and granularity of the F/sub 0/ extraction methods used for adult speech, which, typically are not well suited for infant cries. In general, the fundamental frequency range of adult speech is limited to values below 600 Hz, whereas for infant cries, F/sub 0/ can have a range of several kilo hertz, and be subject to rapid changes in certain cases. This paper presents a new method for accurately determining the F/sub 0/ of infant cries, which should be applicable to other vocalizations as well. The method presented uses the crosscorrelation of adjacent speech segments to generate a three-dimensional plot called a crosscorrelogram. From this plot, the fundamental frequency of an infant cry can easily be extracted, with increased precision.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".