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The recent improvements in recording technology, data storage and batterylife have led to an increased interest in the use of passive acousticmonitoring for a variety of research questions. One of the main obstacles inimplementing wide scale acoustic monitoring programs in terrestrialenvironments is the lack of user-friendly, open source programs for processinglarge sound archives. Here we describe the new, open-source R packageGIBBONFINDR which has functions for detection, classification and visualizationof acoustic signals using a variety of readily available machine learningalgorithms in the R programming environment. We provide a case study showinghow GIBBONFINDR functions can be used in a workflow to detect and classifyBornean gibbon (Hylobates muelleri) calls in long-term acoustic data setsrecorded in Danum Valley Conservation Area, Sabah, Malaysia. Machine learningis currently one of the most rapidly growing fields-- with applications acrossmany disciplines-- and our goal is to make commonly used signal processingtechniques and machine learning algorithms readily available for ecologists whoare interested in incorporating bioacoustics techniques into their research.

GIBBONFINDR: An R package for the detection and classification of acoustic signals