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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

Bar plots of indices values for the three study sites.Plots show indices values for 10 min of dawn chorus. Values for AD, ACI and BI are scaled in the ranges 0:3, 1800:1900 and 0:12 respectively. Point count ‘heard’ data values for each site are given in the end column for comparison.
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fig-2: Bar plots of indices values for the three study sites.Plots show indices values for 10 min of dawn chorus. Values for AD, ACI and BI are scaled in the ranges 0:3, 1800:1900 and 0:12 respectively. Point count ‘heard’ data values for each site are given in the end column for comparison.

Mentions: Values for each of the acoustic indices calculated for the three habitats are given in Table 2 and shown as bar plots in Fig. 2. As we might expect given the minimal anthropogenic noise and broadly similar spectral profile, the NDSI reports near maximum values for each site. The global complexity of each scene is high; it is no surprise then that entropy indices approach 1 and differences between sites are minimal. The ADI reports a small variation, following the rank-order pattern of species heard at each site. Differences between Sueur’s spectral, temporal and therefore overall, H entropy are minimal. ACI similarly shows small variation between sites. This index in particular is very sensitive to the size of the analysis window and requires further exploration to establish which aspects of community composition may be being assessed. BI values report the differences in overall acoustic energy, observable in mean spectrum plot (Fig. 1D), with the highest value at FS, FP being slightly higher than site S. These basic features of the acoustic recordings are at odds with the field observations of abundance and species numbers. An increase in overall energy could be due to certain individuals having intrinsically louder calls, calling more frequently, or simply being closer to the microphone. In validation studies the latter could be countered by factoring in field-based point count distance measures (recorded, but not included here) and call frequencies, as well as tallies of individual vocalisations, the latter being expedited by the use of automatic segmentation software (as in Pieretti, Farina & Morri, 2011).


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)

Bar plots of indices values for the three study sites.Plots show indices values for 10 min of dawn chorus. Values for AD, ACI and BI are scaled in the ranges 0:3, 1800:1900 and 0:12 respectively. Point count ‘heard’ data values for each site are given in the end column for comparison.
© Copyright Policy
Related In: Results  -  Collection

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

fig-2: Bar plots of indices values for the three study sites.Plots show indices values for 10 min of dawn chorus. Values for AD, ACI and BI are scaled in the ranges 0:3, 1800:1900 and 0:12 respectively. Point count ‘heard’ data values for each site are given in the end column for comparison.
Mentions: Values for each of the acoustic indices calculated for the three habitats are given in Table 2 and shown as bar plots in Fig. 2. As we might expect given the minimal anthropogenic noise and broadly similar spectral profile, the NDSI reports near maximum values for each site. The global complexity of each scene is high; it is no surprise then that entropy indices approach 1 and differences between sites are minimal. The ADI reports a small variation, following the rank-order pattern of species heard at each site. Differences between Sueur’s spectral, temporal and therefore overall, H entropy are minimal. ACI similarly shows small variation between sites. This index in particular is very sensitive to the size of the analysis window and requires further exploration to establish which aspects of community composition may be being assessed. BI values report the differences in overall acoustic energy, observable in mean spectrum plot (Fig. 1D), with the highest value at FS, FP being slightly higher than site S. These basic features of the acoustic recordings are at odds with the field observations of abundance and species numbers. An increase in overall energy could be due to certain individuals having intrinsically louder calls, calling more frequently, or simply being closer to the microphone. In validation studies the latter could be countered by factoring in field-based point count distance measures (recorded, but not included here) and call frequencies, as well as tallies of individual vocalisations, the latter being expedited by the use of automatic segmentation software (as in Pieretti, Farina & Morri, 2011).

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