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Bioacoustics in R with warbleR

Bioacoustics research encompasses a wide range of questions, study systems and methods, including the software used for analyses. The warbleR and Rraven packages leverage the flexibility of the R environment to offer a broad and accessible bioinformatics tool set. These packages fundamentally rely upon two types of data to begin bioacoustic analyses in R:

  1. Sound files: Recordings in wav or mp3 format, either from your own research or open-access databases like xeno-canto

  2. Selection tables: Selection tables contain the temporal coordinates (start and end points) of selected acoustic signals within recordings

Package repositories

These packages are both available on CRAN: warbleR, Rraven, as well as on GitHub: warbleR, Rraven. The GitHub repository will always contain the latest functions and updates. You can also check out an article in Methods in Ecology and Evolution documenting the warbleR package [1].

We welcome all users to provide feedback, contribute updates or new functions and report bugs to warbleR’s GitHub repository.

Please note that warbleR and Rraven use functions from the seewave, monitoR, tuneR and dtw packages internally. warbleR and Rraven have been designed to make bioacoustics analyses more accessible to R users, and such analyses would not be possible without the tools provided by the packages above. These packages should be given credit when using warbleR and Rraven by including citations in publications as appropriate (e.g. citation("seewave")).

Parallel processing in warbleR

Parallel processing, or using multiple cores on your machine, can greatly speed up analyses. All iterative warbleR functions now have parallel processing for Linux, Mac and Windows operating systems. These functions also contain progress bars to visualize progress during normal or parallel processing. See [1] for more details about improved running time using parallel processing.

Vignette introduction

In the previous vignette, we used the Rraven package to import Raven selection tables for recordings in our working directory, added more recordings to the data set by downloading new sound files from the open-access xeno-canto database and reviewed methods of automated and manual signal selection in warbleR. Here we continue with the case study of microgeographic vocal variation in long-billed hermit hummingbirds, Phaethornis longirostris [2] by:

  1. Performing quality control processing on selected signals, including visual inspection and tailoring temporal coordinates

  2. Making lexicons for visual classification of signals

This vignette can be run without an advanced understanding of R, as long as you know how to run code in your console. However, knowing more about basic R coding would be very helpful to modify the code for your research questions.

For more details about function arguments, input or output, read the documentation for the function in question (e.g. ?catalog).  

Prepare for analyses


# set your working directory appropriately
# setwd("/path/to/working directory")

# run this if you have restarted RStudio between vignettes without saving your workspace (assuming that you are in your /home/username directory)

# Check your location

This vignette series will not always include all available warbleR functions, as existing functions are updated and new functions are added. To see all functions available in this package:

# The package must be loaded in your working environment

Quality control filtering of selections

Find overlapping selections

Overlapping selections can sometimes arise after selecting signals using other functions or software. The function below helps you detect overlapping signals in your selection table, and has arguments that you can play around with for overlap detection, renaming or deleting overlapping selections.

# To run this example:
# Open Phae_hisnr.csv and modify the start coordinate of the first selection and the end coordinate of the second selection so that the signals overlap

Phae.hisnr <- read.csv("Phae_hisnr.csv", header = TRUE)
head(Phae.hisnr, n = 15)

# yields a data frame with an additional column (ovlp_sels) that indicates which selections overlap
Phae.hisnr <- ovlp_sels(X = Phae.hisnr, max.ovlp = 0)

# run the function again but this time retain only the signals that don't overlap
Phae.hisnr <- ovlp_sels(X = Phae.hisnr, max.ovlp = 0, drop = TRUE)

Make spectrograms of selections

specreator generates spectrograms of individual selected signals. These image files can be used to filter out selections that were poorly made or represent signals that are not relevant to your analysis. This quality control step is important for visualizing your selected signals after any selection method, even if you imported your selections from Raven or Syrinx.

specreator(Phae.hisnr, wl = 300, flim = c(2, 10), it = "jpeg", res = 150, osci = TRUE, ovlp = 90)

Inspect spectrograms and throw away image files that are poor quality to prepare for later steps. Make sure you are working in a directory that only has image files associated with this vignette. Delete the image files corresponding to recording 154070 selection 8 and 154070 selection 12, as the start coordinates for these selections are not accurate.

Remove selections with missing image files

# remove selections after deleting corresponding image files
Phae.hisnr2 <- filtersels(Phae.hisnr, it = "jpeg", incl.wav = TRUE)

After removing the poorest quality selections or signals, there are some other quality control steps that may be helpful.

Check selections

Can selections be read by downstream functions? The function checksels also yields a data frame with columns for duration, minimum samples, sampling rate, channels and bits.

# if selections can be read, "OK" will be printed to check.res column
checksels(Phae.hisnr2, check.header = FALSE)

If selections cannot be read, it is possible the sound files are corrupt. If so, use the fixwavs function to repair wav files.

Tailor temporal coordinates of selections

Sometimes the start and end times of selected signals need fine-tuned adjustments. This is particularly true when signals are found within bouts of closely delivered sounds that may be hard to pull apart, such as duets, or if multiple researchers use different rules-of-thumb to select signals. seltailor provides an interactive interface similar to manualoc for tailoring the temporal coordinates of selections.

If you check out the image files generated by running specreator above, you’ll see that some of the selections made during the automatic detection process with autodetec do not have accurate start and/or end coordinates.

For instance:

The end of this signal is not well selected.  

The temporal coordinates for the tailored signals will be saved in a _ .csv_ file called seltailor_output.csv. You can rename this file and read it back into R to continue downstream analyses.

seltailor(Phae.hisnr2, wl = 300, flim = c(2,10), wn = "hanning", mar = 0.1, osci = TRUE, title = c("sound.files", "selec"), auto.next = TRUE)

# Read in seltailor output after renaming the csv file
Phae.hisnrt <- read.csv("Phae_hisnrt.csv", header = TRUE)
'data.frame':   23 obs. of  6 variables:
 $ sound.files: chr  "Phaethornis-longirostris-154070.wav" "Phaethornis-longirostris-154070.wav" "Phaethornis-longirostris-154070.wav" "Phaethornis-longirostris-154072.wav" ...
 $ selec      : int  14 18 20 36 108 124 142 148 57 83 ...
 $ start      : num  8.66 11.08 12.19 22.6 76.04 ...
 $ end        : num  8.79 11.2 12.32 22.73 76.16 ...
 $ SNR        : num  11.7 11.6 10.2 15.5 15.3 ...
 $ tailored   : chr  "y" "y" "y" "y" ...

Visual classification of selected signals

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.

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", "selec.table"))
writeWave(Phae.long1, "Phae.long1.wav") #save sound files 

# subset selection table
# already contains the frequency range for these signals
st <- selec.table[selec.table$sound.files == "Phae.long1.wav",]
# read wave file as an R object
sgnl <- tuneR::readWave(as.character(st$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)

Optimize spectrogram display parameters

spec_param makes a catalog or mosaic of the same signal plotted with different combinations of spectrogram display arguments. The purpose of this function is to help you choose parameters that yield the best spectrograms (e.g. optimal visualization) for your signals (although low signal-to-noise ratio selections may be an exception).


Make lexicons of signals

This section on catalog is taken from Marcelo Araya-Salas’s bioacoustics GitHub blog with slight modifications.

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
    • facilitates comparisons
  • 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. Check out Marcelo’s post on some recent updates to the catalog function.

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 = "")

Phae.hisnrt$rec_ID <- rec_ID

# 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.imgs(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.


Please cite warbleR when you use the package:

Araya-Salas, M. and Smith-Vidaurre, G. (2017), warbleR: an R package to streamline analysis of animal acoustic signals. Methods Ecol Evol. 8, 184-191.

Reporting bugs

Please report any bugs here.  


  1. Araya-Salas, M. and G. Smith-Vidaurre. 2016. warbleR: an R package to streamline analysis of animal acoustic signals. Methods in Ecology and Evolution. doi: 10.1111/2041-210X.12624