GIBBONFINDR: An R package for the detection and classification of acoustic signals
This provides a user-friendly tool for ecologists to incorporate bioacoustics techniques, addressing a bottleneck in terrestrial acoustic monitoring programs.
The authors tackled the problem of processing large sound archives in passive acoustic monitoring by developing GIBBONFINDR, an open-source R package for detection, classification, and visualization of acoustic signals using machine learning algorithms, and demonstrated its application in detecting and classifying Bornean gibbon calls in long-term data sets from Malaysia.
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.