Machine learning with taxonomic family delimitation aids in the classification of ephemeral beaked whale events in passive acoustic monitoring
Fig 5
Correlation between the total number of clicks per 5-minute bins manually labeled to a class (true class) and total number of clicks classified to predicted class by targeted species pipeline with a hard negative filter in the case study dataset.
Each point is a bin, and each subplot displays all bins manually assigned to a species, along with the class predicted by the DNN in color scale, with dark blue points representing true positives and other colored circles representing misclassified bins. The class abbreviation displayed in bold shows the true class of each subplot. Points along the diagonal show bins for which the number of manually labeled clicks were correctly predicted to a class. Above the diagonal, more clicks were predicted to a class than the true number of clicks per bin, whereas below the diagonal, less clicks were predicted than the total number of true clicks per bin.