Welcome to SoundShape
Here, you will find information on how to implement a promising, and
yet little explored method for biacoustical analysis: the so called
eigensound analysis developed by MacLeod, Krieger and Jones
(2013) and expanded by Rocha & Romano (2021).
Select your acoustic units using Raven Pro Software
Since SoundShape’s version 1.3.0, the function
raven.to.wave allows the creation of ".wav"
files based on selections made using Raven
Pro, which is commonplace in bioacoustical analysis. Topic 3 below
provides information on how to do it.
Introduction on eigensound analysis
Eigensound is a multidisciplinary method focused on the direct
comparison between homologous sounds from different species
(i.e. stereotyped calls/acoustic units; Macleod et
al., 2013; Rocha & Romano, 2021). It consists on applying a
sampling grid over the representation of sound (i.e.
spectrogram data; Figs. 1 and 2) and then translate the spectrogram into
a dataset that can be analyzed similarly to coordinate sets used in
Geometric Morphometrics Methods (GMM). By doing so, eigensound crosses
the bridge between Bioacoustics and GMM.
Despite being well described by Macleod et al. (2013), the
method lacked a free and open platform to run the analysis.
SoundShape package was written on R platform to fill this gap. The
package features functions that enable anyone familiar with
R to easily go from sound waves to principal components
analysis (PCA), using tools extracted from traditional bioacoustics
(i.e. tuneR and seewave packages),
geometric morphometrics (i.e. geomorph package)
and multivariate analysis (e.g. stats
package).
Thanks for using SoundShape and enjoy your reading!
Note: Should you experience problems running any
function, please feel free to report any issues
here.
library(SoundShape)
# Sample data from SoundShape
data(cuvieri)
# Select acoustic unit from sample
cuvieri.cut <- seewave::cutw(cuvieri, f=44100, from = 0.05, to=0.45, output="Wave")
# 3D spectrogram
par(mfrow=c(1,2), mar=c(0,2,1,0))
threeDspectro(cuvieri.cut, flim=c(0, 2.5),
colkey=list(plot=FALSE), cex.axis=0.4, cex.lab=0.8, resfac=3)
# Semilandmarks from sampled surface
threeDspectro(cuvieri.cut, flim=c(0, 2.5), plot.type="points",
samp.grid=TRUE, x.length=70, y.length=50, main="Semilandmarks 3D",
colkey=list(plot=FALSE), cex.axis=0.4, cex.lab=0.8)
Figure 1: Graphical outputs using
threeDspectro function from SoundShape
package: (left) 3D spectrogram and (right) points (i.e.
semilandmarks) sampled from 3D spectrogram data. cuvieri
sample from SoundShape package.
# Traditional oscillogram and spectrogram
par(mfrow=c(1,2), mar=c(4,4,2,1)) # view side by side
seewave::oscillo(cuvieri.cut, title="Oscillogram")
seewave::spectro(cuvieri.cut, flim=c(0, 2.5), grid=FALSE, scale=FALSE, main="Spectrogram")
Figure 2: Graphical outputs using
seewave package: (left) Oscillogram created with
oscillo function and (right) 2D spectrogram created with
spectro function. cuvieri sample from
SoundShape package.
Installation
SoundShape package is currently available on R platform through the
Comprehensive R Archive Network (CRAN) (https://CRAN.R-project.org/package=SoundShape).
Alternatively, a development version is available from GitHub (https://github.com/p-rocha/SoundShape).
The package can be installed using one of the following codes:
# Official version from CRAN (recommended):
install.packages("SoundShape")
# Development version from GitHub:
install.packages("devtools")
devtools::install_github("p-rocha/SoundShape")
Citation
In case you wish to use and cite SoundShape package, use
citation("SoundShape").
citation("SoundShape")
#> To cite package 'SoundShape' in publications use:
#>
#> Rocha P (2024). _SoundShape: Sound Waves Onto Morphometric Data_. R
#> package version 1.3.2, <https://github.com/p-rocha/SoundShape>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {SoundShape: Sound Waves Onto Morphometric Data},
#> author = {Pedro Rocha},
#> year = {2024},
#> note = {R package version 1.3.2},
#> url = {https://github.com/p-rocha/SoundShape},
#> }
In addition, we recommend citing MacLeod et al. (2013) and Rocha
& Romano (2021):
MacLeod, N., Krieger, J., & Jones, K. E. (2013). Geometric
morphometric approaches to acoustic signal analysis in mammalian
biology. Hystrix, the Italian Journal of Mammalogy, 24(1),
110-125. doi: 10.4404/hystrix-24.1-6299.
Rocha, P. & Romano, P. (2021) The shape of sound: A new R
package that crosses the bridge between Bioacoustics and Geometric
Morphometrics. Methods in Ecology and Evolution, 00, 1-7. doi:
10.1111/2041-210X.13580
Workflow using SoundShape package
1. Definition of homologous acoustic units
Since eigensound is centered around stereotyped acoustic units, the
foremost step in sound shape study is the careful definition of units
from which analysis will be conducted. However, even though several
authors argue that acoustic traits constrained by anatomical structures
(e.g. dominant frequency, pulse rate) are more conservative than those
under the direct influence of behaviour (e.g. call duration, interval
between calls), the criteria for homologous sound comparison may vary
across different animal groups (see Rocha & Romano, 2021, for
examples and more on homology between animal sounds). Therefore, we
recommend a throughout literature review before the definition of
acoustic units. Herein, we adopted a physiological definition of ‘note’,
(McLister et al., 1995; Robillard et al., 2006), which led to an
unambiguous selection of stereotyped calls from three frog species:
Physalaemus centralis, P. cuvieri and P.
kroyeri (centralis, cuvieri and
kroyeri sample datas, respectively; Figs. 3 – 5).
# Samples of data from SoundShape package
data(cuvieri)
data(centralis)
data(kroyeri)
# Plot spectro from sample and highlight acoustic units
# centralis
seewave::spectro(centralis, flim = c(0, 4), wl=512, f=44100, ovlp=70, grid=FALSE)
graphics::abline(v=c(0.1, 0.8, 1.08, 1.78, 2.1, 2.8), lty=2)
Figure 3: Spectrogram image of
centralis sample (SoundShape package),
containing a sequence of three stereotyped vocalizations, each
representing a comparable acoustic unit.
# cuvieri
seewave::spectro(cuvieri, flim = c(0,4), wl=512, f=44100, ovlp=70, grid=FALSE)
graphics::abline(v=c(0.05, 0.45, 0.73, 1.13, 1.47, 1.87), lty=2)
Figure 4: Spectrogram image of cuvieri
sample (SoundShape package), containing a sequence of three
stereotyped vocalizations, each representing a comparable acoustic
unit.
# kroyeri
seewave::spectro(kroyeri, flim = c(0, 4), wl=512, f=44100, ovlp=70, grid=FALSE)
graphics::abline(v=c(0.16, 0.96, 1.55, 2.35, 2.9, 3.8), lty=2)
Figure 5: Spectrogram image of kroyeri
sample (SoundShape package), containing a sequence of three
stereotyped vocalizations, each representing a comparable acoustic
unit.
2. Create folder to store acoustic units
The eigensound function (SoundShape
package) focuses on the acquisition of point coordinates (i.e.
semilandmarks) from multiple Waveform Audio File Format
(WAV or WAVE; ".wav" file extensions), with each file
representing a comparable acoustic unit (see section 1). These
".wav" files must be stored on the same folder somewhere in
your computer, which can be created manually at your console and
subsequently assigned as working directory in R.
Alternatively, one can create the folder at e.g. the current
working directory using dir.create. Also create a subfolder
to store the upcoming outputs from eigensound function, as
the following:
# Create temporary folder to store ".wav" files
wav.at <- file.path(base::tempdir(), "original wave")
if(!dir.exists(wav.at)) dir.create(wav.at)
# Create temporary folder to store results
store.at <- file.path(base::tempdir(), "output")
if(!dir.exists(store.at)) dir.create(store.at)
3. Select and store acoustic units as separate ".wav"
files
A reasonable number of comparable acoustic units should be selected
from the sample and stored as new ".wav" files on a folder
from your console (see section 2 for folder paths). Each file should
represent a single unit selected from the original sound wave.
Since the slightest graphical change may incur in biased results
(MacLeod et al., 2013; Rocha & Romano, 2021), acoustic units must be
selected according to optimal signal to noise ratio (i.e.
“clean” recording), and no overlapping frequencies from other
individuals, species, or background noise. Editing and filtering of
sound waves must be restricted to a bare minimum.
The selection can be performed on numerous softwares of acoustic
analysis outside R platform (e.g. Audacity, Raven
Pro), or using some functions from seewave and tuneR packages as
exemplified below:
# Select acoustic units
cut.centralis <- seewave::cutw(centralis, f=44100, from=0, to=0.9, output = "Wave")
cut.cuvieri <- seewave::cutw(cuvieri, f=44100, from=0, to=0.9, output = "Wave")
cut.kroyeri <- seewave::cutw(kroyeri, f=44100, from=0.2, to=1.1, output = "Wave")
# Export ".wav" files containing acoustic units and store on previosly created folder
writeWave(cut.cuvieri, filename = file.path(wav.at, "cut.cuvieri.wav"), extensible = FALSE)
writeWave(cut.centralis, filename = file.path(wav.at, "cut.centralis.wav"), extensible = FALSE)
writeWave(cut.kroyeri, filename = file.path(wav.at, "cut.kroyeri.wav"), extensible = FALSE)
Alternatively, since SoundShape’s version 1.3.0, the
function raven.to.wave allows the creation of
".wav" files based on selections made using Raven
Pro, which is commonplace in bioacoustical analysis. Each selection
(i.e. line in table; see raven.list documentation) should
represent an acoustic unit from the sample study:
###
## Using SoundShape examples:
# Export original sample ".wav" files
tuneR::writeWave(centralis, extensible = TRUE,
filename = file.path(orig.wav, "centralis.wav"))
tuneR::writeWave(cuvieri, extensible = TRUE,
filename = file.path(orig.wav, "cuvieri.wav"))
tuneR::writeWave(kroyeri, extensible = TRUE,
filename = file.path(orig.wav, "kroyeri.wav"))
# Store Raven Pro selection tables at same folder from original ".wav" files
for(i in 1:length(raven.list)){
write.table(raven.list[i], file=file.path(orig.wav, names(raven.list)[i]),
quote=FALSE, sep="\t", row.names = FALSE,
col.names = colnames(raven.list[[i]])) } # end loop
# Verify if folder has both original ".wav" files and Raven's selections
dir(orig.wav)
###
## Start here when using your own recordings
# Export a ".wav" sample for each selection made in Raven Pro
raven.to.wave(orig.wav.folder = orig.wav, wav.samples = wav.at)
# Verify samples
dir(wav.at)
4. Three steps that avoid errors and biased results
The semilandmarks from eigensound function
(SoundShape package) are automatically acquired, meaning
that this approach does not add human errors from LM digitalization.
Despite that, the eigensound protocol (MacLeod et al., 2013) requires
some standardization to ".wav" files that would otherwise
lead to errors or biased results (e.g. sounds overlapping the
edges of a sound window; units far from the beginning of sound window; a
low signal to noise ratio; Rocha & Romano, 2021).
In light of this, Rocha & Romano (2021) proposed the following
steps that prevent errors and biased results, which are aided by the
functions align.wave and eigensound (with
analysis.type = "twoDshape"), both from
SoundShape package.
4.1 Define dimensions for the sound window
First, define the sound window dimensions that encompass the whole
sample of acoustic units. These dimensions are represented by the time
(x-axis) and frequency (y-axis) limits for spectrogram
images, which are respectively defined by the tlim and
flim arguments in eigensound function.
Time limits should be based on the acoustic unit with the longest
duration within the sample, whereas frequency limits should consider the
unit with the largest frequency bandwidth. In the present sample study,
for instance, the longest units are also the ones with broader frequency
bandwidths (i.e. kroyeri sample; Fig. 6), with
approximately 0.7 s duration and highest frequencies close to 3.5 kHz.
Therefore, the sound window dimensions that encompass the whole sample
can be defined with tlim = c(0, 0.8) and
flim = c(0, 4).
This can be exemplified using spectro function from
seewave package:
# Spectrogram plots using standardized sound window dimensions
par(mfrow=c(2,2), mar=c(4,4,2,2))
seewave::spectro(cut.centralis, flim=c(0, 4), tlim=c(0, 0.8), main="data(centralis)",
wl=512, f=44100, ovlp=70, grid=FALSE, scale=FALSE)
seewave::spectro(cut.cuvieri, flim=c(0, 4), tlim=c(0, 0.8), main="data(cuvieri)",
wl=512, f=44100, ovlp=70, grid=FALSE, scale=FALSE)
seewave::spectro(cut.kroyeri, flim=c(0, 4), tlim=c(0, 0.8), main="data(kroyeri)",
wl=512, f=44100, ovlp=70, grid=FALSE, scale=FALSE)
Figure 6: Spectrogram images with standardized sound
window dimensions.
4.2 Alignment of acoustic units at the beginning of a sound
window
The eigensound protocol also requires acoustic units to be placed at
the beginning of a sound window before proceeding with the analysis.
This ensures that variation in each semilandmark is due to energy shifts
within the call, not to changes in their relative position in the sound
window (MacLeod et al., 2013).
Although this arbitrary alignment could be performed on numerous
software of acoustic analysis outside R platform
(e.g. Audacity, Raven
Pro), align.wave function (SoundShape
package) provide an easy alternative to automatically align the units at
the beginning of a sound window whilst also standardizing the durations
of ".wav" files (see section 4.1), thus preventing errors
when running eigensound function.
In order to verify the alignment, run eigensound with
analysis.type = "twoDshape" and
plot.exp = TRUE, which will create 2D spectrogram images
and store them on the folder specified by store.at (see
section 2 for folder paths), a helpful option for the verification of
appropriate alignment and sound window dimensions.
Below is the code employed for the alignment of sound units and
verification of sound window dimensions:
# Place sounds at the beginning of a sound window
align.wave(wav.at=wav.at, wav.to="Aligned", time.length = 0.8)
# Verify alignment using analysis.type = "twoDshape"
eigensound(analysis.type = "twoDshape", wav.at = file.path(wav.at, "Aligned"),
store.at=store.at, plot.exp=TRUE, flim=c(0, 4), tlim=c(0, 0.8))
# Go to folder specified by store.at and check jpeg files created
If either the alignment, or the sound window dimensions, are not
ideal (e.g. units far from the beginning of sound window;
sounds overlapping the edges of sound window), run
align.wave with different values of
time.length and/or time.perc, then use
eigensound to verify the updated spectrogram outputs (see
Rocha & Romano, 2021 for details).
The ideal window dimensions and the alignment of units are often
achieved after a few attempts. If this is troublesome, consider
revisiting the relative amplitude (dBlevel) as the
background noise could be interfering with align.wave (see
section 4.3).
4.3 Set relative amplitude background
Next is the definition of a relative amplitude value
(dBlevel) to be used as background in the 3D spectrogram
(MacLeod et al., 2013). This is an iterative process that can be
implemented by eigensound with
analysis.type = "twoDshape" and
plot.exp = TRUE, and should lead to spectrogram images with
minimum influence from background noise (Rocha & Romano, 2021).
In the present study sample, the curve of relative amplitude was set
at -25 dB (Fig. 5), which is expressed as an absolute value for
dBlevel arguments in SoundShape functions
(i.e. dBlevel = 25).
The code below was used to create the graphs from Fig. 7:
# 2D spectrogram with curves of relative amplitude at -25 dB
par(mfrow=c(1,2), mar=c(4,4,1,1))
s.kro <- seewave::spectro(cut.kroyeri, flim=c(0, 4), tlim = c(0, 0.8),
grid=F, scale=F, f=44100, wl=512, ovlp=70, cont=TRUE,
contlevels = seq(-25, -25, 1), collevels = seq(-40, 0, 0.1))
# 3D spectrogram (with a lower dBlevel for illustrative purpuses)
threeDspectro(cut.kroyeri, dBlevel=40, flim=c(0, 4), tlim=c(0, 0.8), main="",
colkey=list(plot=FALSE), cex.axis=0.4, cex.lab=0.8, resfac=2)
# Set background at -40 dB and remove -Inf values from spectrogram data
for(i in 1:length(s.kro$amp)){if(s.kro$amp[i] == -Inf |s.kro$amp[i] <= -40)
{s.kro$amp[i] <- -40}}
# Add curve of relative amplitude
plot3D::contour3D(x=s.kro$time, y=s.kro$freq, colvar=t(s.kro$amp), z=-25,
plot=T, add=T, addbox=F, col="black", lwd=1.9, nlevels=2, dDepth=0.25)
Figure 7: 2D and 3D spectrograms (left and right,
respectively) with relative amplitude contours highlighted by black
lines (dBlevel = 25). Spectrogram images from
kroyeri sample.
5. Define sampling grid and run eigensound
After concluding the three steps that avoid errors and biased results
(section 4), next is the definition of sampling grid dimensions that
will be used for semilandmark acquisition (i.e. number of cells
per side; x.length and y.length arguments,
eigensound function; Fig. 8).
The number of cells per side will determine the amount of sLM
acquired by eigensound. Therefore, the ideal grid should adequate the
sound sample, while also representing a dimensionality reduction from
spectrogram data (see Rocha & Romano, 2021). Herein, we opted for 70
cells on the time (x-axis, x.length = 70) and 47
cells on the frequency (y-axis, y.length = 47),
which was experimentally defined with the aid of
threeDspectro function, as exemplified below:
# Using threeDspectro to visualize sampling grid
par(mfrow=c(1,2), mar=c(1,2,1,0))
# As "surface"
threeDspectro(cut.kroyeri, samp.grid=TRUE, x.length=70, y.length=47, plot.type="surface",
dBlevel=25, flim=c(0, 4), tlim=c(0, 0.8), f=44100, wl=512, ovlp=70, main="As 'surface'",
colkey=list(plot=FALSE), cex.axis=0.4, cex.lab=0.8)
# As "points"
threeDspectro(cut.kroyeri, samp.grid=TRUE, x.length=70, y.length=47, plot.type="points",
dBlevel=25, flim=c(0, 4), tlim=c(0, 0.8), f=44100, wl=512, ovlp=70, main="As 'points'",
colkey=list(plot=FALSE), cex.axis=0.4, cex.lab=0.8)
Figure 8: Spectrogram data as (left) simplified
surface, and (right) colored semilandmarks acquired from the
intersections of sampling grid (i.e. x.length=70
and y.length=47). Spectrogram images from
kroyeri sample.
5.1 Run eigensound function
Once the three steps that avoid errors are concluded (section 4) and
the sampling grid is defined (see above), it is now possible to acquire
comparable semilandmark coordinates using eigensound
function with analysis.type = "threeDshape".
Results can be simultaneosly assigned to an R object,
and/or stored as the native file format of TPS series (Rohlf,
2015), a ".tps" file to be used by numerous software of
geometric analysis of shape. Here, we focus on the analysis within
R platform, so the results are assigned to the
R object eig.sample, which is available as
sample data from SoundShape.
Note: eig.sample comprises all
vocalizations present in the samples of centralis,
cuvieri and kroyeri, which led to three
acoustic units per species; a total of nine ".wav" files
stored in the same folder. Use help(eig.sample) and check
Rocha & Romano (2021) for details.
In the following code, eigensound is run with a
logarithmic scale on the time axis (i.e.
log.scale = TRUE; see Rocha & Romano, 2021):
# Sample semilandmarks for each ".wav" file on a folder using a logarithmic sampling grid
# Export 3D graphs with semilandmarks as colored points for inspection
eig.sample <- eigensound(analysis.type="threeDshape", dBlevel=25,
f=44100, wl=512, ovlp=70, flim=c(0, 4), tlim=c(0, 0.8),
x.length=70, y.length=47, log.scale=TRUE, plot.exp=TRUE, plot.type="points",
wav.at=file.path(wav.at, "Aligned"), store.at=store.at)
# Go to folder specified by store.at and check jpeg files created
6. Principal Components Analysis
After employing a sampling grid to acquire semilandmarks from sound
waves (section 5), the eigensound protocol proceeds to a dimensionality
reduction procedure that facilitate comparison of sound shape data.
Herein, we opted for a Principal Components Analysis (PCA), which allow
complex sound waves to be described and plotted onto major axes (PCs)
encompassing the majority of variance within the sample (MacLeod et al.,
2013; Rocha & Romano, 2021).
The PCA can be performed using prcomp function
(stats package), as exemplified below:
# PCA using three-dimensional semilandmark coordinates embeeded in eig.sample
pca.eig.sample <- stats::prcomp(geomorph::two.d.array(eig.sample))
# View summary results
summary(pca.eig.sample)
#> Importance of components:
#> PC1 PC2 PC3 PC4 PC5 PC6
#> Standard deviation 125.0412 101.4575 39.39182 29.97205 17.11317 14.5307
#> Proportion of Variance 0.5407 0.3560 0.05367 0.03107 0.01013 0.0073
#> Cumulative Proportion 0.5407 0.8967 0.95041 0.98148 0.99161 0.9989
#> PC7 PC8 PC9
#> Standard deviation 4.95910 2.63948 3.841e-14
#> Proportion of Variance 0.00085 0.00024 0.000e+00
#> Cumulative Proportion 0.99976 1.00000 1.000e+00
Note: At this point, consider employing a stopping
rule to select which PCs should be retained as nontrivial and
interpretable, and which ones should be ignored (e.g. broken
stick models, vegan package)
(Jackson, 1993; see Rocha & Romano, 2021 for details).
6.1 Hypothetical sound shape configurations from semilandmark
data
Before proceeding to the ordination of Principal Components (PCs),
the eigensound protocol also includes hypothetical sound surfaces to be
interpreted along with the ordination plots (MacLeod et al., 2013).
These surfaces are calculated using hypo.surf function and
should be interpreted along with the ordination plots, enhancing the
comprehension of how sound shape changed along with each PCs
In SoundShape package, the hypothetical sound shapes can
be created using hypo.surf function, which enables the
calculation of either the mean shape configuration from the sample
(i.e. consensus shape; Zelditch et al., 2012), or minimum and
maximum deformations relative to PCs, as exemplified below:
# Create hypothetical sound surfaces using hypo.surf
# Mean shape configuration (consensus)
hypo.surf(eig.sample, PC="mean", flim=c(0, 4), tlim=c(0, 0.8), x.length=70, y.length=47,
cex.lab=0.7, cex.axis=0.5, cex.main=1)
Figure 9: Hypothetical sound surface (acquired using
hypo.surf function) representing mean shape configuration
from eig.sample sample of data.
# Minimum and maximum deformations - Principal Component 1
hypo.surf(eig.sample, PC=1, flim=c(0, 4), tlim=c(0, 0.8), x.length=70, y.length=47,
cex.lab=0.7, cex.axis=0.5, cex.main=1)
Figure 10: Hypothetical sound surfaces (acquired
using hypo.surf function) representing minimum and maximum
deformations relative to PC1 in the PCA featuring
eig.sample sample of data.
# Minimum and maximum deformations - Principal Component 2
hypo.surf(eig.sample, PC=2, flim=c(0, 4), tlim=c(0, 0.8), x.length=70, y.length=47,
cex.lab=0.7, cex.axis=0.5, cex.main=1)
Figure 11: Hypothetical sound surfaces (acquired
using hypo.surf function) representing minimum and maximum
deformations relative to PC2 in the PCA featuring
eig.sample sample of data.
6.2 Ordination plot with pca.plot function
Among the benefits of employing a PCA on multivariate data is the
possibility to generate ordination plots encompassing the majority of
variation embedded in the sample (Fig. 12). These plots simplify
description and are widely employed in exploratory data analysis,
specially when one is looking for potential groups within the sample
(Zelditch et al. 2012).
The ordination plot is facilitated by pca.plot function
(SoundShape package), which require the output of a PCA
performed by prcomp function (stats package)
and a vector with groups to be colored.
The code chunk below exemplifies how create an ordination plot using
pca.plot:
# PCA using semilandmark coordinates
pca.eig.sample <- stats::prcomp(geomorph::two.d.array(eig.sample))
# Verify names of acoustic units from sample
dimnames(eig.sample)[[3]]
#> [1] "cut.cent1" "cut.cent2" "cut.cent3" "cut.cuv1" "cut.cuv2" "cut.cuv3"
#> [7] "cut.kro1" "cut.kro2" "cut.kro3"
# Based on those names, create factor to use as groups in subsequent ordination plot
sample.gr <- factor(c(rep("centralis", 3), rep("cuvieri", 3), rep("kroyeri", 3)))
# Ordination plot
pca.plot(pca.eig.sample, groups=sample.gr, conv.hulls=sample.gr, leg.pos="bottomright", cex=1.2)
Figure 12: Ordination plot using
eig.sample data acquired from the samples of
centralis, cuvieri and
kroyeri.
7. Interpreting the outputs of SoundShape
In order to fully comprehend how sound shape changes along the
studied sample, the PCA outcome should be interpreted along with the
visualization of hypothetical sound shapes (Figs. 9 – 11) and the
ordination plot (Fig. 12).
The ordination plot (Fig. 12) represent 89.7% of the whole variance
in our dataset, which yielded a clear structuring of units from
different species. In addition, the hypothetical sound surfaces from the
main axis of variation (i.e. mean shape and PCs) clearly
represented the sound shapes of acoustic units employed in the
study.
The higher positive values of PC1, for instance, corresponded to
acoustic units with clear harmonic structure and broad frequency
bandwidth (Fig. 10), a hypothetical sound shape remarkably similar to
three-dimensional spectrograms from kroyeri sample. Not
coincidently, the units from kroyeri scored high positive
PC1 values. Lower and negative values of PC1, on the other hand, were
less obvious, with a hypothetical shape that gather sonic information
from cuvieri and centralis samples, both
species with negative PC1 scores. A similar pattern is observed in PC2
axis (Fig. 11), with positive PC2 values referring to broad frequency
bandwidth and no harmonic structure (i.e.
centralis sample), and negative PC2 values representing
short durations and clear harmonic structure (i.e.
cuvieri sample).
References
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