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%. s o m m a i r ePour la collecte et l'analyse de vocalises de mamifres marins, l'extraction de la fondamentale est une tape cruciale.Dans le cas des dauphins, nous pouvons considrer qu'il y a deux types de sons voiss : les chants qui peuvent tre dcrits comme des tonalits AM-FM, et les rafales ("bursts") constitues de signaux hautement harmoniques.La premire des trois difficults pour extraire le timbre est le trs faible rapport signal sur bruit d aux reflections multiples et autres interfrences.La seconde consiste apprhender les rsolutions harmoniques sur le signal de ctacs par rapport aux traitements connus en parole par exemple.Dans ce papier, nous testons notre nouvelle mthode d'extraction de timbre sur un modle Chane de Markov Cache Hirarchique partir de coefficient cepstraux.Nous comparons nos rsultats la mthode YIN base sur un calcul d'autocorrlation, et get_f0 qui est extracteur de timbre classique par programmation dynamique utilisant des coefficients LPC.Nous montrons que notre mthode apporte un gain de 10% par rapport ces mthodes.
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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 | 0.000 |
| 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".