Parametric and holistic approaches to analyzing primate vocal signals for acoustic markers of caller identity, emotional state, and external context.
Bibliographic record
Abstract
Vocal signals can be acoustically differentiated according to a variety of intrinsic and extrinsic dimensions. Among the most commonly investigated sources of signal differentiation in primates are those that mark the identity of the caller, potential variation in the caller’s underlying emotional or motivational state, and external environmental factors associated with signaling (e.g., the presense of predators or food). We analyzed a corpus of grunt vocalizations recorded from wild baboons for acoustic cues associated with these three dimensions. Grunts were collected from eight adult female baboons in two different behavioral contexts: 1. When approaching other females to handle their infants, and; 2. When embarking on an extended foraging excursion. Both contexts were further subdivided into conditions associated with low- versus high-arousal for callers. Analysis of the signals involved two different approaches: 1. A parametric analysis of specific acoustic features logically connected to either vocal-fold activity or supralaryngeal filtering (i.e., Fo and resonance properties) combined with principle components analysis (PCA) dimension reduction and discriminant analysis call classification; 2. A holistic analysis of the global spectral structure of entire signals that combined a PCA-based eigenvector decomposition and neural network classification of the calls. We compare results of the two analysis strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".