A new direction for Soundscape Ecology? Toward the extraction and evaluation of ecologically-meaningful soundscape objects using sparse coding methods
- Published
- Accepted
- Subject Areas
- Adaptive and Self-Organizing Systems, Data Mining and Machine Learning, Data Science
- Keywords
- Soundscape Ecology, Conservation biology, sparse coding, MIR, ecology, rapid biodiversity assessment
- Copyright
- © 2015 Eldridge et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. A new direction for Soundscape Ecology? Toward the extraction and evaluation of ecologically-meaningful soundscape objects using sparse coding methods. PeerJ PrePrints 3:e1407v1 https://doi.org/10.7287/peerj.preprints.1407v1
Abstract
Efficient methods of biodiversity assessment and monitoring are central to ecological research and crucial in conservation management; technological advances in remote acoustic sensing inspire new approaches. In line with the emerging field of Soundscape Ecology (Pijanowski et al., 2011), the acoustic approach is based on the rationale that the ecological processes occurring within a landscape are tightly linked to and reflected in the high-level structure of the patterns of sounds emanating from those landscapes – the soundscape. Rather than attempting to recognise species-specific calls, either manually or automatically, analysis of the high-level structure of the soundscape tackles the problem of diversity assessment at the community (rather than species) level (Pijanowski et al., 2011; Farina, 2014). Preliminary work has attempted to make a case for community-level acoustic indices (e.g. Pieretti et al., 2011; Farina, 2014; Sueur et al., 2008); existing indices provide simple statistical summaries of the frequency or time domain signal. We suggest that under this approach, the opportunity to analyse spectro-temporal structural information is diminished, limiting power both as monitoring and investigative tools. In this paper we consider sparse-coding and source separation algorithms (SIPLCA-2D) as a means to access and summarise ecologically-meaningful sound objects. In doing so we highlight a possible new approach for understanding and assessing ecologically relevant interactions within the conceptual framework of Soundscape Ecology.
Author Comment
The ideas in this paper were presented at ERMITES2015 - a workshop on big data sciences for bioacoustic environmental survey April 21 2015 Toulon. Submission to PeerJ Comp Science is planned.
Supplemental Information
Audio example 1: Primary Forest at SLR
Audio excerpt as shown in Figure 1 (top spectrogram)
Audio example 1: Secondary Forest at SLR
Audio excerpt as shown in Figure 1 (middle spectrogram)
Audio example 1: Silvopasture at SLR
Audio excerpt as shown in Figure 1 (bottom spectrogram)