Using a Novel Visualization Tool for Rapid Survey of Long-Duration Acoustic Recordings for Ecological Studies of Frog Chorusing

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.

Visualization and Quantification of the Activities of Animal Vocalizations in Forest Species Using Robot Audition Techniques

“…As a seminal example, the falsecolour spectrogram for longdurations proposed by Towsey et al (2014) enabled users to identify species in large audio datasets and intuitively grasp the changes in dynamics of the acoustic signals by showing the spectrogram coloured according an acoustic index, which reflects the specific properties of the species vocalizations. Brodie et al (2022) further explored the efficacy of using falsecolour spectrograms to identify multiple frog species in longduration recordings and obtained data on the chorusing activity of the frog community. They found that frog choruses of different species could be visually identified with high accuracy in falsecolour spectrogams based on acoustic indices after a training phase.…”

Using acoustic indices in ecology: Guidance on study design, analyses and interpretation

“…See Section 6. Rapidly visualising large data sets to facilitate qualitative interpretation. For example, false‐colour spectrograms can condense years of data into a single plot, which permits identification of dominant soundscape patterns and seasonal changes (Brodie et al, 2022; Phillips et al, 2018; Towsey et al, 2018; Towsey, Zhang, et al, 2014). Detecting strong temporal and spatial trends, aiding identification of large‐scale soundscape patterns and areas of acoustic interest (e.g.…”

“…Wider testing has demonstrated that associating acoustic indices with biodiversity metrics, such as species richness, diversity or abundance, is not straightforward. There has been some success in linking acoustic indices to the presence of individual species (Brodie et al, 2022; Papin et al, 2019; Towsey et al, 2018; Znidersic et al, 2020), or to broader taxon richness (Allen‐Ankins et al, 2023; Bradfer‐Lawrence et al, 2020; Dröge et al, 2021; Roca & Van Opzeeland, 2020). However, there are contradictory patterns reported in the literature.…”