Analytical Methods in Ecoacoustics

Gage, Stuart H.; Towsey, Michael; Kasten, Eric P.

doi:10.1002/9781119230724.ch16

Cited by 12 publications

(8 citation statements)

References 31 publications

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“…Acoustic indices are mathematical functions designed to evaluate some aspects of the acoustic diversity (Sueur et al, ). They compute specific features of the spectrum or waveform thought to represent meaningful information about biodiversity (Gage, Towsey, & Kasten, ). In this study, we employed three acoustic indices: the acoustic complexity index ( ACI ), the spectral entropy ( H f ), and the median of amplitude envelope ( M ).…”

Section: Methodsmentioning

confidence: 99%

Spatio‐temporal heterogeneity in river sounds: Disentangling micro‐ and macro‐variation in a chain of waterholes

et al. 2019

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Passive acoustic monitoring is gaining momentum as a viable alternative method to surveying freshwater ecosystems. As part of an emerging field, the spatio‐temporal replication levels of these sampling methods need to be standardised. However, in shallow waters, acoustic spatio‐temporal patchiness remains virtually unexplored. In this paper, we specifically investigate the spatial heterogeneity in underwater sounds observed within and between waterholes of an ephemeral river at different times of the day and how it could affect sampling in passive acoustic monitoring. We recorded in the Einasleigh River, Queensland in August 2016, using a linear transect of hydrophones mounted on frames. We recorded four times a day: at dawn, midday, dusk, and midnight. To measure different temporal and spectral attributes of the recorded sound, we investigated the mean frequency spectrum and computed acoustic indices. Both mean frequency spectrum and index analyses revealed that the site and diel activity patterns significantly influenced the sounds recorded, even for adjacent sites with similar characteristics along a single river. We found that most of the variation was due to temporal patterns, followed by between‐site differences, while within‐site differences had limited influence. This study demonstrates high spatio‐temporal acoustic variability in freshwater environments, linked to different species or species groups. Decisions about sampling design are vital to obtain adequate representation. This study thus emphasises the need to tailor spatio‐temporal settings of a sampling design to the aim of the study, the species and the habitat.

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Section: Methodsmentioning

confidence: 99%

Spatio‐temporal heterogeneity in river sounds: Disentangling micro‐ and macro‐variation in a chain of waterholes

et al. 2019

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“…We study the spectral content and frequency dynamics of the recordings using averaged power spectra as well as normalised spectrograms for all nine settings. Another problem in environmental acoustics exhibits from the visualisation and analysis of long period time–frequency data, as typical spectrograms (e.g., temporal scale of 0.02 s/frame and one frame per pixel column) cannot be displayed on standard monitors, even for only one 24 h recording [ 31 ]. Therefore, we additionally propose a method using frequency correlation matrices (FCMs) to analyse the urban AE, by capturing links between the variability in different frequency bands.…”

Section: Introductionmentioning

confidence: 99%

Analysing Interlinked Frequency Dynamics of the Urban Acoustic Environment

Haselhoff

Braun

Hornberg

et al. 2022

IJERPH

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As sustainable metropolitan regions require more densely built-up areas, a comprehensive understanding of the urban acoustic environment (AE) is needed. However, comprehensive datasets of the urban AE and well-established research methods for the AE are scarce. Datasets of audio recordings tend to be large and require a lot of storage space as well as computationally expensive analyses. Thus, knowledge about the long-term urban AE is limited. In recent years, however, these limitations have been steadily overcome, allowing a more comprehensive analysis of the urban AE. In this respect, the objective of this work is to contribute to a better understanding of the time–frequency domain of the urban AE, analysing automatic audio recordings from nine urban settings over ten months. We compute median power spectra as well as normalised spectrograms for all settings. Additionally, we demonstrate the use of frequency correlation matrices (FCMs) as a novel approach to access large audio datasets. Our results show site-dependent patterns in frequency dynamics. Normalised spectrograms reveal that frequency bins with low power hold relevant information and that the AE changes considerably over a year. We demonstrate that this information can be captured by using FCMs, which also unravel communities of interlinked frequency dynamics for all settings.

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“…A single index reveals limited information about the structure of biological activity and other acoustic events in a soundscape. However, the LDFC spectrogram approach solves this problem by combining these three indices to create a visualisation of complex soundscape structure (Gage et al., ). Acoustic indices may emerge independently (ACI, ENT, or EVN), as a combination of two indices (ACI‐EVN, ACI‐ENT, or EVN‐ENT), or as a combination of all indices (ACI‐ENT‐EVN) and appear as a variety of different colours and patterns in an LDFC spectrogram.…”

Section: Methodsmentioning

confidence: 99%

“…Acoustic indices are relatively simple to generate, do not require call templates or expertise to develop, and can serve as effective proxies for broad‐scale biological activity (Pieretti, Farina, & Morri, ) and species richness (Fuller, Axel, Tucker, & Gage, ; Towsey, Wimmer, Williamson, & Roe, ). Coarse‐scale temporal summaries ranging from a few seconds to a minute can also be created by combining acoustic indices, emphasising specific soundscape features (Gage, Towsey, & Kasten, ). Furthermore, selected acoustic indices can be combined to create long‐duration, false‐colour (LDFC) spectrograms.…”

Section: Introductionmentioning

confidence: 99%

Assessing the value of acoustic indices to distinguish species and quantify activity: A case study using frogs

et al. 2018

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Soundscapes can provide information about a wide range of habitats and species through the recording of vocalisations over long temporal scales. Because of the large volumes of data collected, computational approaches, such as the application of acoustic indices, are required to extract useful information from long‐duration recordings. Acoustic indices summarise various soundscape features into frequency ranges over defined time intervals and can aid in the visual exploration, detection, and analysis of species vocalisation patterns. Here, we examine the performance of combinations of three acoustic indices commonly used in visual exploration, the acoustic complexity index, the temporal entropy spectrum index, and the event spectrum index, and assess their ability to distinguish species and describe acoustic features commonly used to detect species and analyse activity. Our case study focuses on three frog species with distinct call structures from Bickerton Island, Northern Territory, Australia. Call structure was categorised based on the number of pulses and harmonics. We summarised acoustic activity by calculating acoustic indices in 256 equal‐sized bins over the entire the frequency spectrum, for 30‐s intervals, and found that acoustic index values could be used to distinguish species and describe acoustic features. The acoustic complexity index was the most effective index for distinguishing species. To describe acoustic features, we examined correlations between acoustic index values and summarised acoustic features, including call rate, total duration, loudness and signal‐to‐noise ratio. In single‐pulsed species with no harmonics, we found spectral index values were significantly and sometimes strongly correlated with acoustic features. In comparison, species with harmonics were found to be weakly and less frequently correlated with acoustic features even if sampled calls were loud and have high signal‐to‐noise ratio. We suggest that acoustic indices have the potential to describe acoustic features in single‐pulsed species but are limited in those with harmonics. We conclude that acoustic indices can be a useful tool to distinguish some anuran species and to broadly understand specific acoustic features used to analyse calling activity over long periods of time. Further research is required to better understand the relationships between acoustic indices and acoustic features to determine the general utility of indices to detect and distinguish audible species and to identify other acoustic features of various taxa.

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Analytical Methods in Ecoacoustics

Cited by 12 publications

References 31 publications

Spatio‐temporal heterogeneity in river sounds: Disentangling micro‐ and macro‐variation in a chain of waterholes

Spatio‐temporal heterogeneity in river sounds: Disentangling micro‐ and macro‐variation in a chain of waterholes

Analysing Interlinked Frequency Dynamics of the Urban Acoustic Environment

Assessing the value of acoustic indices to distinguish species and quantify activity: A case study using frogs

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