Visual classification of signals is fundamental to vocal repertoire
analysis, and can also be useful for other questions. If your research
focuses on assessing variation between individuals or groups, several
warbleR functions can provide you with important
information about how to steer your analysis. If there is discrete
variation in vocalization structure across groups (e.g. treatments or
geographic regions), visual classification of vocalizations will be
useful.
Print long spectrograms with full_spectrograms
The function full_spectrogramsthat we used in the last
vignette can also be a tool for visually classifying signals. Long
spectrograms can be printed to classify signals by hand, or comments
accompanying the selections can be printed over selected signals.
Here, we print the start and end of selections with a red dotted
line, and the selection number printed over the signal. If a selection
data frame contains a comments column, these will be printed with the
selection number.
# highlight selected signals
full_spectrograms(Phae.hisnrt, wl = 300, flim = c(2, 10), ovlp = 10, sxrow = 6, rows = 15, it = "jpeg")
# concatenate full_spectrograms image files into a single PDF per recording
# full_spectrograms images must be jpegs
full_spectrograms2pdf(keep.img = FALSE, overwrite = TRUE)
Check out the image file in your working directory. These will look
very similar to the full_spectrograms images produced in
vignette 1, but with red dotted lines indicating where the selected
signals start and end.
Highlight spectrogram regions with color_spectro
color_spectro allows you to highlight selections you’ve
made within a short region of a spectrogram. In the example below we
will use color_spectro to highlight neighboring songs. This
function has a wide variety of uses, and could be especially useful for
analysis of duets or coordinated singing bouts. This example is taken
directly from the color_spectro documentation. If working
with your own data frame of selections, make sure to calculate the
frequency range for your selections beforehand using the function
frange, which will come up in the next vignette.
# we will use Phaethornis songs and selections from the warbleR package
data(list = c("Phae.long1", "lbh_selec_table"))
writeWave(Phae.long1, "Phae.long1.wav") # save sound files
# subset selection table
# already contains the frequency range for these signals
st <- lbh_selec_table[lbh_selec_table$sound.files == "Phae.long1.wav", ]
# read wave file as an R object
sgnl <- read_sound_file(as.character(st$sound.files[1]))
# create color column
st$colors <- c("red2", "blue", "green")
# highlight selections
color_spectro(wave = sgnl, wl = 300, ovlp = 90, flim = c(1, 8.6), collevels = seq(-90, 0, 5), dB = "B", X = st, col.clm = "colors", base.col = "skyblue", t.mar = 0.07, f.mar = 0.1)
Make lexicons of signals
When we are interested in geographic variation of acoustic signals,
we usually want to compare spectrograms from different individuals and
sites. This can be challenging when working with large numbers of
signals, individuals and/or sites. catalog aims to simplify
this task.
This is how it works:
catalog plots a matrix of spectrograms from signals
listed in a selection table- the catalog files are saved as image files in the working directory
(or path provided)
- Several image files are generated if the signals do not fit in a
single file
- Spectrograms can be labeled or color-tagged to facilitate exploring
variation related to the parameter of interest (e.g. site or song type
if already classified)
- A legend can be added to help match colors with tag levels
- different color palettes can be used for each tag
- The duration of the signals can be “fixed” such that all the
spectrograms have the same duration
- You can control the number of rows and columns as well as the width
and height of the output image
catalog allows you to group signals into biologically
relevant groups by coloring the background of selected spectrograms
accordingly. There is also an option to add hatching to tag labels, as
well as filling the catalog with spectrograms by rows or columns of the
selection table data frame, among other additional arguments.
The move_imgs function can come in handy when creating
multiple catalogs to avoid overwriting previous image files, or when
working through rounds of other image files. In this case, the first
catalog we create has signals labeled, tagged and grouped with
respective color and hatching levels. The second catalog we create will
not have any grouping of signals whatsoever, and could be used for a
test of inter-observer reliability. move_imgs helps us move
the first catalog into another directory to save it from being
overwritten when creating the second catalog.
# create a column of recording IDs for friendlier catalog labels
rec_ID <- sapply(1:nrow(Phae.hisnrt), function(x) {
gsub(x = strsplit(as.character(Phae.hisnrt$sound.files[x]), split = "-")[[1]][3], pattern = ".wav$", replacement = "")
})
rec_ID
Phae.hisnrt$rec_ID <- rec_ID
str(Phae.hisnrt)
# set color palette
# alpha controls transparency for softer colors
cmc <- function(n) cm.colors(n, alpha = 0.8)
catalog(X = Phae.hisnrt, flim = c(2, 10), nrow = 4, ncol = 3, height = 10, width = 10, tag.pal = list(cmc), cex = 0.8, same.time.scale = TRUE, mar = 0.01, wl = 300, gr = FALSE, labels = "rec_ID", tags = "rec_ID", hatching = 1, group.tag = "rec_ID", spec.mar = 0.4, lab.mar = 0.8, max.group.cols = 5)
catalog2pdf(keep.img = FALSE, overwrite = TRUE)
# assuming we are working from the warbleR_example directory
# the ~/ format does not apply to Windows
# make sure you have already moved or deleted all other pdf files
move_images(from = ".", it = "pdf", create.folder = TRUE, folder.name = "Catalog_image_files")
Catalog with labels, tags and groups
You can also make lexicons for blind scoring, which could be useful
for determining interobserver reliability.
# now create a catalog without labels, tags, groups or axes
Phae.hisnrt$no_label <- ""
# catalog(X = Phae.hisnrt, flim = c(1, 10), nrow = 4, ncol = 3, height = 10, width = 10, cex = 0.8, same.time.scale = TRUE, mar = 0.01, wl = 300, spec.mar = 0.4, rm.axes = TRUE, labels = "no_label", lab.mar = 0.8, max.group.cols = 5, img.suffix = "nolabel")
catalog(X = Phae.hisnrt, flim = c(1, 10), nrow = 4, ncol = 3, height = 10, width = 10, tag.pal = list(cmc), cex = 0.8, same.time.scale = TRUE, mar = 0.01, wl = 300, gr = FALSE, labels = "no_label", spec.mar = 0.4, lab.mar = 0.8, max.group.cols = 5, img.suffix = "nolabels")
catalog2pdf(keep.img = FALSE, overwrite = TRUE)
Next vignette: Acoustic (dis)similarity, coordinated singing
and simulating songs
Here we finished the second phase of the warbleR
workflow, which includes various options for quality control filtering
or visual classification of signals that you can leverage during
acoustic analysis. After running the code in this vignette, you should
now have an idea of how to:
- how to perform quality control filtering of your selected signals,
including visual inspection and tailoring temporal coordinates of
spectrograms
- use different methods for visual classification of signals,
including:
- long spectrograms
- highlighted regions within spectrograms
- catalogs or lexicons of individual signals
The next vignette will cover the third phase of the warbleR
workflow, which includes methods to perform acoustic measurements as a
batch process, an example of how to use these measurements for an
analysis of geographic variation, coordinated singing analysis and a new
function to simulate songs.
