A comparison of pitch extraction methodologies for dolphin vocalization
Bibliographic record
Abstract
When collecting and analyzing marine mammal vocalizations one of the most important goals is to automatically extract the pitch/fundamental frequency of the collected calls. In dolphins we can assume that there are two main pitched sounds: whistles, which can be described as tonal AM-FM signals, and bursts, which can be described as highly harmonic signals. There are three main difficulties with pitch extraction on dolphin vocalizations that arise from the nature of the data. First, most underwater recordings are restricted to a low signal-to-noise ratio due to reflections, hardware noise and other interferences. This constitutes a big challenge for most existing pitch trackers. Second, one has to take into account the significant differences in the frequency range of bottlenose dolphin vocalizations compared to humans. Finally, dolphin whistles and bursts generally are emitted in two distinct frequency ranges, which result in different modes in the analysis data. In this work we compare our novel pitch extraction approach with two widely popular algorithms. Our approach uses hierarchy-based hidden Markov models (HMM) with cepstral coefficients as features. We quantitatively compare the performance of our algorithm with Yin, which is based on a modified autocorrelation method and get_f0, a popular off-the-shelf pitch tracker that utilizes linear predictive coefficients (LPC) and dynamic programming. Our approach outperforms the comparative methods by at least a factor of 10%.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".