  Passive acoustics provides a powerful tool for monitoring the endangered
North Atlantic right whale ($Eubalaena$ $glacialis$), but robust detection
algorithms are needed to handle diverse and variable acoustic conditions and
differences in recording techniques and equipment. Here, we investigate the
potential of deep neural networks for addressing this need. ResNet, an
architecture commonly used for image recognition, is trained to recognize the
time-frequency representation of the characteristic North Atlantic right whale
upcall. The network is trained on several thousand examples recorded at various
locations in the Gulf of St.\ Lawrence in 2018 and 2019, using different
equipment and deployment techniques. Used as a detection algorithm on fifty
30-minute recordings from the years 2015-2017 containing over one thousand
upcalls, the network achieves recalls up to 80%, while maintaining a precision
of 90%. Importantly, the performance of the network improves as more variance
is introduced into the training dataset, whereas the opposite trend is observed
using a conventional linear discriminant analysis approach. Our work
demonstrates that deep neural networks can be trained to identify North
Atlantic right whale upcalls under diverse and variable conditions with a
performance that compares favorably to that of existing algorithms.
