A new method for ecoacoustics? Toward the extraction and evaluation of ecologically-meaningful soundscape components using sparse coding methods

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Eldridge, A; Casey, Michael A.; Moscoso, P and Peck, M. 2016. A new method for ecoacoustics? Toward the extraction and evaluation of ecologically-meaningful soundscape components using sparse coding methods. PeerJ, 4, e2108. ISSN 2167-8359 [Article]

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Official URL: https://peerj.com/articles/2108/

Abstract or Description

Passive acoustic monitoring is emerging as a promising non-invasive proxy for ecological complexity with potential as a tool for remote assessment and monitoring (Sueur & Farina, 2015). Rather than attempting to recognise species-specific calls, either manually or automatically, there is a growing interest in evaluating the global acoustic environment. Positioned within the conceptual framework of ecoacoustics, a growing number of indices have been proposed which aim to capture community-level dynamics by (e.g., Pieretti, Farina & Morri, 2011; Farina, 2014; Sueur et al., 2008b) by providing statistical summaries of the frequency or time domain signal. Although promising, the ecological relevance and efficacy as a monitoring tool of these indices is still unclear. In this paper we suggest that by virtue of operating in the time or frequency domain, existing indices are limited in their ability to access key structural information in the spectro-temporal domain. Alternative methods in which time-frequency dynamics are preserved are considered. Sparse-coding and source separation algorithms (specifically, shift-invariant probabilistic latent component analysis in 2D) are proposed as a means to access and summarise time-frequency dynamics which may be more ecologically-meaningful.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.7717/peerj.2108

Additional Information:

The research was funded by Leverhulme Trust Research Project Grant RPG-2014-403. The data collection was funded by University of Sussex Research Development Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Keywords:

Soundscape ecology, Rapid biodiversity assessment, Ecoacoustics, Automated methods, Sparse coding, Unsupervised learning, Acoustic niche hypothesis, Probabilistic latent component analysis

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