A machine learning pipeline for classification of cetacean echolocation clicks in large underwater acoustic datasets
Fig 9
Spectra of detections counted as network misclassifications in the balanced Site H evaluation dataset.
Spectra are sorted by classification probability scores shown along the upper edge of each subplot. A positive relationship between signal amplitude and probability scores is apparent, with higher amplitude signals being assigned higher probability labels by the network. Many of the signals counted as misclassifications appear to have been correctly classified by the network, but were likely incorrectly labeled in the unsupervised step used to create the training and evaluation datasets. For instance, the majority of spectra misclassified as Risso’s dolphin or PWS type A appear to have been correctly assigned to those species respectively.