GIBBONR: An R package for the detection and classification of acoustic signals using machine learning

1. 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 acoustic data. 2. Here we describe the new, open-source R package GIBBONR which has functions for classification, detection and visualization of acoustic signals using different readily available machine learning algorithms in the R programming environment. 3. We provide a case study showing how GIBBONR functions can be used in a workflow to classify and detect Bornean gibbon (Hylobates muelleri) calls in long-term recordings from Danum Valley Conservation Area, Sabah Malaysia. 4. 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.