Abstract
Continuous recording of environmental sounds could allow long-term monitoring of vocal wildlife, and scaling of ecological studies to large temporal and spatial scales. However, such opportunities are currently limited by constraints in the analysis of large acoustic data sets. Computational methods and automation of call detection require specialist expertise and are time consuming to develop, therefore most biological researchers continue to use manual listening and inspection of spectrograms to analyze their sound recordings. False-color spectrograms were recently developed as a tool to allow visualization of long-duration sound recordings, intending to aid ecologists in navigating their audio data and detecting species of interest. This paper explores the efficacy of using this visualization method to identify multiple frog species in a large set of continuous sound recordings and gather data on the chorusing activity of the frog community. We found that, after a phase of training of the observer, frog choruses could be visually identified to species with high accuracy. We present a method to analyze such data, including a simple R routine to interactively select short segments on the false-color spectrogram for rapid manual checking of visually identified sounds. We propose these methods could fruitfully be applied to large acoustic data sets to analyze calling patterns in other chorusing species.
Highlights
Passive acoustic monitoring is a standard technique in the ecologist’s toolkit for monitoring and studying the acoustic signals of animals in their natural habitats (Gibb et al, 2019; Sugai et al, 2019)
The time taken to survey the nightly recordings using the R routine to select, cut and open short segments of audio ranged from a few seconds to 90 min
Visualization of long sound recordings is an innovative approach for providing insight into the acoustic structure of environmental soundscapes, and to aid detection of wildlife vocalizations
Summary
Passive acoustic monitoring is a standard technique in the ecologist’s toolkit for monitoring and studying the acoustic signals of animals in their natural habitats (Gibb et al, 2019; Sugai et al, 2019). Autonomous sound recorders provide significant opportunities to monitor wildlife over long time frames, and at greater scale than can be done physically in the field. Continuous and large-scale acoustic monitoring has become feasible as technological advances have provided smaller, cheaper recording units with improved power and storage capacities. The large streams of acoustic data that can be collected must be mined for ecologically meaningful data, and so the problem of scaling of observations has been translated into a problem of scaling data analysis (Gibb et al, 2019). Ecologists using acoustic approaches require effective and efficient sound analysis tools that enable them to take advantage of the scaling opportunities in large acoustic data sets
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