Towards the topology of autoencoder of calls versus clicks of marine mammal
Bibliographic record
Abstract
The goal is to learn the features and the representation adapted for cetacean sound dynamics without any priors. Thus, we develop data driven model to generate voicing and click of cetaceans audio signals. We learn representation and features of stationary or nonstationary emission using neural network from raw audio. We use different types of convolutions (causal, with strides, with dilation [1]), or gradient inversion [2]. Experiments are conducted on various kind of calls of humpback whales from nips4b challenge [3] or Orca whale. We compare the topology for transient encoding on Physeters and Inia g. For each model, we detail the resulting filters and discuss on the topology. We acknowledge Region PACA and NortekMED for Roger’s Phd grant, & DGA and Région Haut de France for Ferrari’s Phd grant. [1] Oord, Dieleman, Zen, Simonyan, Vinyals, Graves et al. Wavenet : A generative model for raw audio, arXiv:1609.03499, 2016 [2] Balestriero, Roger, Glotin, Baraniuk, Semi-Supervised Learning via New Deep Network Inversion, arXiv:1711.04313, 2017 [3] Glotin, LeCun, Mallat et al. Proc. 1st wkp on Neural Information Processing for Bioacoustics NIPS4B, joint to NIPS Alberta USA, 2013 http://sabiod.org/nips4b/challenge2.html, http://sabiod.org/NIPS4B2013_book.pdf
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".