Interpreting the latent representations of a convolutional neural network trained on spectrograms
Bibliographic record
Abstract
Recent work [1,2] has shown that Convolutional Neural Networks (CNNs) trained on spectrograms of acoustic signals are capable of learning high-level latent representations for the purpose of detecting and classifying the vocalizations of endangered baleen whales. The aforementioned latent representations were used in the development of an automated system that was capable of detecting the vocalizations of blue, fin, and sei whales against non-biological and ambient noise sources to a high degree of accuracy (0.961, F-1 Score = 0.899). In this work, we conduct an exploratory analysis of the same latent representations using statistical machine learning approaches as well as by visualizing the convolutional feature maps learned by the CNN. Through this analysis we attempt to interpret what properties of a spectrogram are easily and/or most often exploited by the CNN during training in order to improve upon the state-of-the-art and develop more robust detection systems going forward. [1] M. Thomas, B. Martin, K. Kowarski, B. Gaudet, and S. Matwin, Marine Mammal Species Classification using Convolutional Neural Networks and a Novel Acoustic Representation, ECML PKDD 2019 (Springer, Cham, 2019). [2] M. Thomas, "Towards a novel data representation for classifying acoustic signals," in Canadian Conference on Artificial Intelligence (Springer, Cham, 2019).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".