A New Method for Ecoacoustics? Toward the Extraction and Evaluation of Ecologically-Meaningful Sound Objects using Sparse Coding Methods
- Published
- Accepted
- Subject Areas
- Bioinformatics, Adaptive and Self-Organizing Systems, Data Mining and Machine Learning, Data Science
- Keywords
- Soundscape Ecology, Conservation biology, sparse coding, rapid biodiversity assessment, Sparse Coding, Unsupervised Learning, Biodiversity, Acoustic Niche Hypothesis, Ecoacoustics, Probabilistic Latent Component Analysis
- Copyright
- © 2016 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
- 2016. A New Method for Ecoacoustics? Toward the Extraction and Evaluation of Ecologically-Meaningful Sound Objects using Sparse Coding Methods. PeerJ Preprints 4:e1407v3 https://doi.org/10.7287/peerj.preprints.1407v3
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 perspectives in ecology: environmental sound monitoring is emerging as a reliable non-invasive proxy for ecological complexity (Sueur and Farina, 2015). Rather than attempting to recognise species-specific calls, either manually or automatically, we are interested in monitoring the global acoustic environment, tackling the problem of diversity assessment at the community (rather than species) level. Preliminary work has attempted to make a case for community-level acoustic indices (e.g. Pieretti et al., 2011; Farina, 2014; Sueur et al., 2008b) which 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 their power both as monitoring and investigative tools. In this paper we consider sparse-coding and source separation algorithms (specifically, shift-invariant probabilistic latent component analysis in 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 ecoacoustics.
Author Comment
This is a revision version. This is a submission to PeerJ for review.
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)