#
warbleR: Visual inspection and signal classification

####
Marcelo Araya-Salas, PhD

####
Grace Smith-Vidaurre

####
“2020-06-29”

## 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

    library(warbleR)

    # 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)
    setwd(file.path(getwd(),"warbleR_example"))

    # Check your location
    getwd()

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
    ls("package:warbleR")

## 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)
    str(Phae.hisnr)
    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)
    nrow(Phae.hisnr2)

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)
    str(Phae.hisnrt)

    '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.

### Print long spectrograms with `lspec`

The function `lspec`that 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 (for instance, if you used with `manualoc` argument `selcomm` during the selection process).

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
    lspec(Phae.hisnrt, wl = 300, flim = c(2, 10), ovlp = 10, sxrow = 6, rows = 15, it = "jpeg")

    # concatenate lspec image files into a single PDF per recording
    # lspec images must be jpegs
    lspec2pdf(keep.img = FALSE, overwrite = TRUE)

Check out the image file in your working directory. These will look very similar to the `lspec` 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", "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 = "")
    })
    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.imgs(from = ".", it = "pdf", create.folder = TRUE, folder.name = "Catalog_image_files")

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.

## Citation

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.

References

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