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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.

The recent improvements in recording technology, data storage and battery life have led to an increased interest in the use of passive acoustic monitoring for a variety of research questions. One of the main obstacles in implementing wide scale acoustic monitoring programs in terrestrial environments is the lack of user-friendly, open source programs for processing large sound archives. Here we describe the new, open-source R package GIBBONFINDR which has functions for detection, classification and visualization of acoustic signals using a variety of readily available machine learning algorithms in the R programming environment. We provide a case study showing how GIBBONFINDR functions can be used in a workflow to detect and classify Bornean gibbon (Hylobates muelleri) calls in long-term acoustic data sets recorded in Danum Valley Conservation Area, Sabah, Malaysia. Machine learning is currently one of the most rapidly growing fields-- with applications across many disciplines-- and our goal is to make commonly used signal processing techniques and machine learning algorithms readily available for ecologists who are interested in incorporating bioacoustics techniques into their research., Comment: R package