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