Automatic detection and classification of baleen whale social calls using convolutional neural networksa)

Passive acoustic monitoring has proven to be an indispensable tool for many aspects of baleen whale research. Manual detection of whale calls on these large data sets demands extensive manual labor. Automated whale call detectors offer a more efficient approach and have been developed for many species and call types. However, calls with a large level of variability such as fin whale (Balaenoptera physalus) 40 Hz call and blue whale (B. musculus) D call have been challenging to detect automatically and hence no practical automated detector exists for these two call types. Using a modular approach consisting of faster region-based convolutional neural network followed by a convolutional neural network, we have created automated detectors for 40 Hz calls and D calls. Both detectors were tested on recordings with high- and low density of calls and, when selecting for detections with high classification scores, they were shown to have precision ranging from 54% to 57% with recall ranging from 72% to 78% for 40 Hz and precision ranging from 62% to 64% with recall ranging from 70 to 73% for D calls. As these two call types are produced by both sexes, using them in long-term studies would remove sex-bias in estimates of temporal presence and movement patterns.

Automated summary

Passive acoustic monitoring is crucial for baleen whale research, but manual detection of whale calls in large data sets is labor-intensive. Automated detectors have been developed for many whale species, but detecting calls with high variability, such as fin whale 40 Hz calls and blue whale D calls, remains a challenge. By using a modular approach with convolutional neural networks, researchers have created automated detectors for these challenging call types with precision and recall rates ranging from 54% to 64%.