  Automated detection of acoustic signals is crucial for effective monitoring
of vocal animals and their habitats across ecologically-relevant spatial and
temporal scales. Recent advances in deep learning have made these approaches
more accessible. However, there are few deep learning approaches that can be
implemented natively in the R programming environment; approaches that run
natively in R may be more accessible for ecologists. The "torch for R"
ecosystem has made the use of transfer learning with convolutional neural
networks accessible for R users. Here, we evaluate a workflow that uses
transfer learning for the automated detection of acoustic signals from passive
acoustic monitoring (PAM) data. Our specific goals include: 1) present a method
for automated detection of gibbon calls from PAM data using the "torch for R"
ecosystem; 2) compare the results of transfer learning for six pretrained CNN
architectures; and 3) investigate how well the different architectures perform
on datasets of the female calls from two different gibbon species: the northern
grey gibbon (Hylobates funereus) and the southern yellow-cheeked crested gibbon
(Nomascus gabriellae). We found that the highest performing architecture
depended on the test dataset. We successfully deployed the top performing model
for each gibbon species to investigate spatial of variation in gibbon calling
behavior across two grids of autonomous recording units in Danum Valley
Conservation Area, Malaysia and Keo Seima Wildlife Sanctuary, Cambodia. The
fields of deep learning and automated detection are rapidly evolving, and we
provide the methods and datasets as benchmarks for future work.
