Automatic recognition of harmonic bird sounds using a frequency track extraction algorithm
Bibliographic record
Abstract
This paper demonstrates automatic recognition of vocalizations of four common bird species (herring gull [Larus argentatus], blue jay [Cyanocitta cristata], Canada goose [Branta canadensis], and American crow [Corvus brachyrhynchos]) using an algorithm that extracts frequency track sets using track properties of importance and harmonic correlation. The main result is that a complex harmonic vocalization is rendered into a set of related tracks that is easily applied to statistical models of the actual bird vocalizations. For each vocalization type, a statistical model of the vocalization was created by transforming the training set frequency tracks into feature vectors. The extraction algorithm extracts sets of frequency tracks from test recordings that closely approximate harmonic sounds in the file being processed. Each extracted set in its final form is then compared with the statistical models generated during the training phase using Mahalanobis distance functions. If it matches one of the models closely, the recognizer declares the set an occurrence of the corresponding vocalization. The method was evaluated against a test set containing vocalizations of both the 4 target species and 16 additional species as well as background noise containing planes, cars, and various natural sounds.
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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 | 0.001 |
| 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".