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

Eldridge A, Casey M, Moscoso P, Peck M - PeerJ (2016)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Evolution, Behaviour and Environment, University of Sussex , Brighton , East Sussex , UK.

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.

No MeSH data available.


Related in: MedlinePlus

SIPLCA2 outputs for Silvopasture site dawn chorus.Entropy (S) values are shown in brackets. Original Spectrum (A) and Component Reconstructions (B), Individual Component Reconstructions (C), Time-Frequency Kernels (D) and Activation (shift-time) Functions (E).
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fig-6: SIPLCA2 outputs for Silvopasture site dawn chorus.Entropy (S) values are shown in brackets. Original Spectrum (A) and Component Reconstructions (B), Individual Component Reconstructions (C), Time-Frequency Kernels (D) and Activation (shift-time) Functions (E).

Mentions: Full outputs for all three sites using the SI-PLCA2 algorithm with dual 2D dictionaries are shown in Figs. 4, 5 and 6. Each 10 min site recording is sampled, taking 16 time windows from across the file of around 4 s each, arranged in order. The input is the log-frequency spectrogram of these samples, as before. Extensive analysis of larger data sets across more diverse soundscapes is needed before we can begin to evaluate the ecological significance or application of this approach, but a number of promising observations can be made. As can be seen in Figs. 4A, 5A, 6A, the component reconstructions appear faithful to the original spectrogram. The individual component reconstructions (Figs. 4C, 5C, 6C) pull out clearly distinct components. This is clearest in S3 (Fig. 6C) where the first component is broadband ambient noise, and each of components 1–5 appear as distinct ‘voices’ grouped according to both spectral range and spectro-temporal periodic gesture.


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

Eldridge A, Casey M, Moscoso P, Peck M - PeerJ (2016)

SIPLCA2 outputs for Silvopasture site dawn chorus.Entropy (S) values are shown in brackets. Original Spectrum (A) and Component Reconstructions (B), Individual Component Reconstructions (C), Time-Frequency Kernels (D) and Activation (shift-time) Functions (E).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4933085&req=5

fig-6: SIPLCA2 outputs for Silvopasture site dawn chorus.Entropy (S) values are shown in brackets. Original Spectrum (A) and Component Reconstructions (B), Individual Component Reconstructions (C), Time-Frequency Kernels (D) and Activation (shift-time) Functions (E).
Mentions: Full outputs for all three sites using the SI-PLCA2 algorithm with dual 2D dictionaries are shown in Figs. 4, 5 and 6. Each 10 min site recording is sampled, taking 16 time windows from across the file of around 4 s each, arranged in order. The input is the log-frequency spectrogram of these samples, as before. Extensive analysis of larger data sets across more diverse soundscapes is needed before we can begin to evaluate the ecological significance or application of this approach, but a number of promising observations can be made. As can be seen in Figs. 4A, 5A, 6A, the component reconstructions appear faithful to the original spectrogram. The individual component reconstructions (Figs. 4C, 5C, 6C) pull out clearly distinct components. This is clearest in S3 (Fig. 6C) where the first component is broadband ambient noise, and each of components 1–5 appear as distinct ‘voices’ grouped according to both spectral range and spectro-temporal periodic gesture.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Evolution, Behaviour and Environment, University of Sussex , Brighton , East Sussex , UK.

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.

No MeSH data available.


Related in: MedlinePlus