title: A new method for ecoacoustics? Toward the extraction and evaluation of ecologically-meaningful soundscape components using sparse coding methods.
abstract: 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.
authors.affiliation: Department of Evolution, Behaviour and Environment, University of Sussex , Brighton , East Sussex , UK.
authors.forename: Alice
authors.initials: A
authors.lastname: Eldridge
authors.affiliation: Departments of Music and Computer Science, Dartmouth College , Hanover, NH , United States.
authors.forename: Michael
authors.initials: M
authors.lastname: Casey
authors.affiliation: Department of Evolution, Behaviour and Environment, University of Sussex , Brighton , East Sussex , UK.
authors.forename: Paola
authors.initials: P
authors.lastname: Moscoso
authors.affiliation: Department of Evolution, Behaviour and Environment, University of Sussex , Brighton , East Sussex , UK.
authors.forename: Mika
authors.initials: M
authors.lastname: Peck
keywords: Acoustic niche hypothesis; Automated methods; Ecoacoustics; Probabilistic latent component analysis; Rapid biodiversity assessment; Soundscape ecology; Sparse coding; Unsupervised learning