Automated Detection of Gibbon Calls From Passive Acoustic Monitoring Data Using Convolutional Neural Networks in the "Torch for R" Ecosystem.

ABSTRACT: Automated detection of acoustic signals is crucial for effective monitoring of sound-producing animals and their habitats across ecologically relevant spatial and temporal scales. Recent advances in deep learning have made these approaches more accessible. However, few deep learning approaches 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 deep learning with convolutional neural networks (CNNs) accessible for R users. Here, we evaluate a workflow for the automated detection and classification 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

SUBMITTER: Clink DJ 

PROVIDER: S-EPMC12259389 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature