# Annotation data format

#### Marcelo Araya-Salas, PhD

#### 2025-10-21

This vignette explains in detail the structure of the R data objects containing sound file annotations that are required by the package warbleR.

An annotation table (or selection table in Raven’s and warbleR’s terminology) is a data set that contains information about the location in time (and sometimes in frequency) of the sounds of interest in one or more sound files. warbleR can take sound file annotations represented in the following R objects:

- Data frames
- Selection tables
- Extended selection tables

The last 2 are annotation specific R classes included in warbleR. Here we described the basic structure of these objects and how they can be created.

# Data frames

Data frames with sound file annotations must contain the following columns:

1. sound.files: character or factor column with the name of the sound files including the file extension (e.g. “rec_1.wav”)
2. selec: numeric, character or factor column with a unique identifier (at least within each sound file) for each annotation (e.g. 1, 2, 3 or “a”, “b”, “c”)
3. start: numeric column with the start position in time of an annotated sound (in seconds)
4. end: numeric column with the end position in time of an annotated sound (in seconds)

sound.files | selec | start | end
--- | --- | --- | ---
sound_file_1.wav | 1 | 3.02 | 5.58
sound_file_1.wav | 2 | 7.92 | 9.00
sound_file_2.wav | 1 | 4.21 | 5.34
sound_file_2.wav | 2 | 8.85 | 11.57

Data frames containing annotations can also include the following optional columns:

- 1. bottom.freq: numeric column with the bottom frequency of the frequency range of the annotation (in kHz)
- 1. top.freq: numeric column with the top frequency of the frequency range of the annotation (in kHz)
- 1. channel: numeric column with the number of the channel in which the annotation is found in a multi-channel sound file (optional, by default is 1 if not supplied)

sound.files | selec | start | end | bottom.freq | top.freq | channel
--- | --- | --- | --- | --- | --- | ---
sound_file_1.wav | 1 | 3.02 | 5.58 | 5.46 | 10.22 | 1
sound_file_1.wav | 2 | 7.92 | 9.00 | 3.73 | 9.36 | 1
sound_file_2.wav | 1 | 4.21 | 5.34 | 4.31 | 9.40 | 1
sound_file_2.wav | 2 | 8.85 | 11.57 | 4.55 | 9.11 | 1

The sample data “lbh_selec_table” contains a data frame with annotations with the format expected by warbleR:

    library(warbleR)

    data("lbh_selec_table")

    knitr::kable(lbh_selec_table)

sound.files | channel | selec | start | end | bottom.freq | top.freq
--- | --- | --- | --- | --- | --- | ---
Phae.long1.wav | 1 | 1 | 1.169 | 1.342 | 2.22 | 8.60
Phae.long1.wav | 1 | 2 | 2.158 | 2.321 | 2.17 | 8.81
Phae.long1.wav | 1 | 3 | 0.343 | 0.518 | 2.22 | 8.76
Phae.long2.wav | 1 | 1 | 0.160 | 0.292 | 2.32 | 8.82
Phae.long2.wav | 1 | 2 | 1.457 | 1.583 | 2.28 | 8.89
Phae.long3.wav | 1 | 1 | 0.627 | 0.758 | 3.01 | 8.82
Phae.long3.wav | 1 | 2 | 1.974 | 2.104 | 2.78 | 8.89
Phae.long3.wav | 1 | 3 | 0.123 | 0.255 | 2.32 | 9.31
Phae.long4.wav | 1 | 1 | 1.517 | 1.662 | 2.51 | 9.22
Phae.long4.wav | 1 | 2 | 2.933 | 3.077 | 2.58 | 10.23
Phae.long4.wav | 1 | 3 | 0.145 | 0.290 | 2.58 | 9.74

Typically, annotations are created in other sound analysis programs (mainly, Raven, Avisoft, Syrinx and Audacity) and then imported into R. We recommend annotating sound files in Raven sound analysis software (Cornell Lab of Ornithology) and then importing them into R with the package Rraven. This package facilitates data exchange between R and Raven sound analysis software and allow users to import annotation data into R using the warbleR annotation format (see argument ‘warbler.format’ in the function `imp_raven()`).

Data frames containing annotations always refer to sound files. Therefore, when using data frames users must always indicate the location of the sound files to every warbleR function when providing annotations in this format.

# warbleR annotation formats

## Selection tables

These objects are created with the `selection_table()` function. The function takes data frames containing annotation data as in the format described above. Therefore the same mandatory and optional columns are used by selection tables. The function verifies if the information is consistent (see the function `check_sels()` for details) and saves the ‘diagnostic’ metadata as an attribute in the output object. Selection tables are basically data frames in which the information contained has been double checked to ensure it can be read by other warbleR functions.

Selection tables are created by the function `selection_table()`:

    # write example sound files in temporary directory
    writeWave(Phae.long1, file.path(tempdir(), "Phae.long1.wav"))
    writeWave(Phae.long2, file.path(tempdir(), "Phae.long2.wav"))
    writeWave(Phae.long3, file.path(tempdir(), "Phae.long3.wav"))
    writeWave(Phae.long4, file.path(tempdir(), "Phae.long4.wav"))

    st <-
      selection_table(X = lbh_selec_table, path = tempdir())

    knitr::kable(st)

    all selections are OK

sound.files | channel | selec | start | end | bottom.freq | top.freq
--- | --- | --- | --- | --- | --- | ---
Phae.long1.wav | 1 | 1 | 1.169 | 1.342 | 2.22 | 8.60
Phae.long1.wav | 1 | 2 | 2.158 | 2.321 | 2.17 | 8.81
Phae.long1.wav | 1 | 3 | 0.343 | 0.518 | 2.22 | 8.76
Phae.long2.wav | 1 | 1 | 0.160 | 0.292 | 2.32 | 8.82
Phae.long2.wav | 1 | 2 | 1.457 | 1.583 | 2.28 | 8.89
Phae.long3.wav | 1 | 1 | 0.627 | 0.758 | 3.01 | 8.82
Phae.long3.wav | 1 | 2 | 1.974 | 2.104 | 2.78 | 8.89
Phae.long3.wav | 1 | 3 | 0.123 | 0.255 | 2.32 | 9.31
Phae.long4.wav | 1 | 1 | 1.517 | 1.662 | 2.51 | 9.22
Phae.long4.wav | 1 | 2 | 2.933 | 3.077 | 2.58 | 10.23
Phae.long4.wav | 1 | 3 | 0.145 | 0.290 | 2.58 | 9.74

Selection table is an specific object class:

    class(st)

    [1] "selection_table" "data.frame"

They have their own printing method:

    st

    Object of class 'selection_table'
    * The output of the following call:
    selection_table(X = lbh_selec_table, path = tempdir(), pb = FALSE)

    Contains:
    *  A selection table data frame with 11 rows and 7 columns:
    sound.files       channel   selec   start     end   bottom.freq
    ---------------  --------  ------  ------  ------  ------------
    Phae.long1.wav          1       1   1.169   1.342          2.22
    Phae.long1.wav          1       2   2.158   2.321          2.17
    Phae.long1.wav          1       3   0.343   0.518          2.22
    Phae.long2.wav          1       1   0.160   0.292          2.32
    Phae.long2.wav          1       2   1.457   1.583          2.28
    Phae.long3.wav          1       1   0.627   0.758          3.01
    ... 1 more column(s) (top.freq)
     and 5 more row(s)

    * A data frame (check.results) with 11 rows generated by check_sels() (as an attribute)
    created by warbleR 1.1.37

Note that the path to the sound files must be provided. This is necessary in order to verify that the data supplied conforms to the characteristics of the audio files.

Selection table also refer to sound files. Therefore, users must always indicate the location of the sound files to every warbleR function when providing annotations in this format

## Extended selection tables

Extended selection tables are annotations that include both the acoustic and annotation data. This is an specific object class, extended_selection_table, that includes a list of ‘wave’ objects corresponding to each of the selections in the data. Therefore, the function transforms the selection table into self-contained objects since the original sound files are no longer needed to perform most of the acoustic analysis in warbleR. This can facilitate the storage and exchange of (bio)acoustic data. This format can also speed up analyses, since it is not necessary to read the sound files every time the data is analyzed.

The `selection_table()` function also creates extended selection tables. To do this, users must set the argument `extended = TRUE` (otherwise, the class would be a selection table). The following code converts the example ‘lbh_selec_table’ data into an extended selection table:

    est <- selection_table(
      X = lbh_selec_table,
      pb = FALSE,
      extended = TRUE,
      path = tempdir()
    )

    all selections are OK

Extended selection table is an specific object class:

    class(est)

    [1] "extended_selection_table" "data.frame"

The class has its own printing method:

    est

    Object of class 'extended_selection_table'
    * The output of the following call:
    selection_table(X = lbh_selec_table, path = tempdir(), extended = TRUE,  pb = FALSE)

    Contains:
    *  A selection table data frame with 11 row(s) and 7 columns:
    sound.files         channel   selec   start     end   bottom.freq
    -----------------  --------  ------  ------  ------  ------------
    Phae.long1.wav_1          1       1     0.1   0.273          2.22
    Phae.long1.wav_2          1       1     0.1   0.263          2.17
    Phae.long1.wav_3          1       1     0.1   0.275          2.22
    Phae.long2.wav_1          1       1     0.1   0.233          2.32
    Phae.long2.wav_2          1       1     0.1   0.226          2.28
    Phae.long3.wav_1          1       1     0.1   0.231          3.01
    ... 1 more column(s) (top.freq)
     and 5 more row(s)

    * 11 wave object(s) (as attributes):
    Phae.long1.wav_1, Phae.long1.wav_2, Phae.long1.wav_3, Phae.long2.wav_1, Phae.long2.wav_2, Phae.long3.wav_1
    ... and 5 more

    * A data frame (check.results) with 11 rows generated by check_sels() (as an attribute)

    The selection table was created by element (see description in '?selection_table')
    * 1 sampling rate(s) (in kHz): 22.5
    * 1 bit depth(s): 16
    * Created by warbleR 1.1.37

### Handling extended selection tables

Several functions can be used to deal with objects of this class. First can test if the object belongs to the extended_selection_table:

    is_extended_selection_table(est)

    [1] TRUE

You can subset the selection in the same way that any other data frame and it will still keep its attributes:

    est2 <- est[1:2, ]

    is_extended_selection_table(est2)

    [1] TRUE

As shown above, there is also a generic version of `print()` for this class of objects:

    ## print (equivalent to `print(est)`)
    est

    Object of class 'extended_selection_table'
    * The output of the following call:
    selection_table(X = lbh_selec_table, path = tempdir(), extended = TRUE,  pb = FALSE)

    Contains:
    *  A selection table data frame with 11 row(s) and 7 columns:
    sound.files         channel   selec   start     end   bottom.freq
    -----------------  --------  ------  ------  ------  ------------
    Phae.long1.wav_1          1       1     0.1   0.273          2.22
    Phae.long1.wav_2          1       1     0.1   0.263          2.17
    Phae.long1.wav_3          1       1     0.1   0.275          2.22
    Phae.long2.wav_1          1       1     0.1   0.233          2.32
    Phae.long2.wav_2          1       1     0.1   0.226          2.28
    Phae.long3.wav_1          1       1     0.1   0.231          3.01
    ... 1 more column(s) (top.freq)
     and 5 more row(s)

    * 11 wave object(s) (as attributes):
    Phae.long1.wav_1, Phae.long1.wav_2, Phae.long1.wav_3, Phae.long2.wav_1, Phae.long2.wav_2, Phae.long3.wav_1
    ... and 5 more

    * A data frame (check.results) with 11 rows generated by check_sels() (as an attribute)

    The selection table was created by element (see description in '?selection_table')
    * 1 sampling rate(s) (in kHz): 22.5
    * 1 bit depth(s): 16
    * Created by warbleR 1.1.37

You can also split them and/or combine them by rows. Here the original extended_selection_table is divided using indexing into two objects and combine the two back into a single object using `rbind()`:

    est3 <- est[1:5, ]

    est4 <- est[6:11, ]

    est5 <- rbind(est3, est4)

    # print
    est5

    Object of class 'extended_selection_table'
    * The output of the following call:
    rbind(deparse.level, ..1, ..2)

    Contains:
    *  A selection table data frame with 11 row(s) and 7 columns:
    sound.files         channel   selec   start     end   bottom.freq
    -----------------  --------  ------  ------  ------  ------------
    Phae.long1.wav_1          1       1     0.1   0.273          2.22
    Phae.long1.wav_2          1       1     0.1   0.263          2.17
    Phae.long1.wav_3          1       1     0.1   0.275          2.22
    Phae.long2.wav_1          1       1     0.1   0.233          2.32
    Phae.long2.wav_2          1       1     0.1   0.226          2.28
    Phae.long3.wav_1          1       1     0.1   0.231          3.01
    ... 1 more column(s) (top.freq)
     and 5 more row(s)

    * 11 wave object(s) (as attributes):
    Phae.long1.wav_1, Phae.long1.wav_2, Phae.long1.wav_3, Phae.long2.wav_1, Phae.long2.wav_2, Phae.long3.wav_1
    ... and 5 more

    * A data frame (check.results) with 11 rows generated by check_sels() (as an attribute)

    The selection table was created by element (see description in '?selection_table')
    * 1 sampling rate(s) (in kHz): 22.5
    * 1 bit depth(s): 16
    * Created by warbleR 1.1.37

The resulting extended selection table contains the same data as the original extended selection table:

    # same annotations
    all.equal(est, est5, check.attributes = FALSE)

    [1] TRUE

    # same acoustic data
    all.equal(attr(est, "wave.objects"), attr(est5, "wave.objects"))

    [1] TRUE

The ‘wave’ objects can be read individually using `read_sound_file()`, a wrapper for the `readWave()` function from tuneR, which can handle extended selection tables:

    wv1 <- read_sound_file(X = est, index = 3, from = 0, to = 0.37)

These are regular ‘wave’ objects:

    class(wv1)

    [1] "Wave"
    attr(,"package")
    [1] "tuneR"

    wv1

    Wave Object
        Number of Samples:      8325
        Duration (seconds):     0.37
        Samplingrate (Hertz):   22500
        Channels (Mono/Stereo): Mono
        PCM (integer format):   TRUE
        Bit (8/16/24/32/64):    16

    # print spectrogram
    seewave::spectro(
      wv1,
      wl = 150,
      grid = FALSE,
      scale = FALSE,
      ovlp = 90,
      palette = viridis::viridis,
      collevels = seq(-100, 0 , 5)
    )

    par(mfrow = c(3, 2), mar = rep(0, 4))

    for (i in 1:6) {
      wv <- read_sound_file(
        X = est,
        index = i,
        from = 0.05,
        to = 0.32
      )

      seewave::spectro(
        wv,
        wl = 150,
        grid = FALSE,
        scale = FALSE,
        axisX = FALSE,
        axisY = FALSE,
        ovlp = 90,
        palette = viridis::viridis,
        collevels = seq(-100, 0 , 5)
      )
    }

The `read_sound_file()` function requires a selection table, as well as the row index (i.e. the row number) to be able to read the ‘wave’ objects. It can also read a regular ‘wave’ file if the path is provided.

Note that other functions that modify data frames are likely to delete the attributes in which the ‘wave’ objects and metadata are stored. For example, the merge and the extended selection box will remove its attributes:

    # create new data frame
    Y <-
      data.frame(
        sound.files = est$sound.files,
        site = "La Selva",
        lek = c(rep("SUR", 5), rep("CCL", 6))
      )

    # combine
    mrg_est <- merge(est, Y, by = "sound.files")

    # check class
    is_extended_selection_table(mrg_est)

    [1] FALSE

In this case, we can use the `fix_extended_selection_table()` function to transfer the attributes of the original extended selection table:

    # fix est
    mrg_est <- fix_extended_selection_table(X = mrg_est, Y = est)

    # check class
    is_extended_selection_table(mrg_est)

    [1] TRUE

This works as long as some of the original sound files are retained and no other selections are added.

Finally, these objects can be used as input for most warbleR functions, without the need of refering to any sound file. For instance we can easily measure acoustic parameters on data in extended_selection_table format using the function `spectro_analysis()`:

    #  parametros espectrales
    sp <- spectro_analysis(est)

    # check first 10 columns
    sp[, 1:10]

sound.files | selec | duration | meanfreq | sd | freq.median | freq.Q25 | freq.Q75 | freq.IQR | time.median
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Phae.long1.wav_1 | 1 | 0.173 | 5.98 | 1.40 | 6.33 | 5.30 | 6.87 | 1.57 | 0.080
Phae.long1.wav_2 | 1 | 0.163 | 6.00 | 1.42 | 6.21 | 5.33 | 6.88 | 1.55 | 0.082
Phae.long1.wav_3 | 1 | 0.175 | 6.02 | 1.51 | 6.42 | 5.15 | 6.98 | 1.83 | 0.094
Phae.long2.wav_1 | 1 | 0.133 | 6.40 | 1.34 | 6.60 | 5.61 | 7.38 | 1.77 | 0.074
Phae.long2.wav_2 | 1 | 0.126 | 6.31 | 1.37 | 6.60 | 5.61 | 7.21 | 1.60 | 0.084
Phae.long3.wav_1 | 1 | 0.131 | 6.61 | 1.09 | 6.67 | 6.06 | 7.34 | 1.27 | 0.058
Phae.long3.wav_2 | 1 | 0.130 | 6.64 | 1.12 | 6.67 | 6.11 | 7.43 | 1.32 | 0.072
Phae.long3.wav_3 | 1 | 0.131 | 6.58 | 1.25 | 6.65 | 6.03 | 7.39 | 1.36 | 0.058
Phae.long4.wav_1 | 1 | 0.145 | 6.22 | 1.48 | 6.23 | 5.46 | 7.30 | 1.85 | 0.087
Phae.long4.wav_2 | 1 | 0.144 | 6.46 | 1.59 | 6.34 | 5.63 | 7.57 | 1.94 | 0.087
Phae.long4.wav_3 | 1 | 0.145 | 6.12 | 1.54 | 6.08 | 5.18 | 7.24 | 2.06 | 0.087

### ‘By element’ vs ‘by song’ extended selection tables

As mention above extended selection tables by default contain one wave object for each annotation (i.e. row):

    length(attr(est, "wave.objects")) == length(unique(paste(est$sound.files)))

    [1] TRUE

This default behavior generates a ‘by element’ extended selection table, as each resulting wave object contains a single element (usually defined as continuous traces of power spectral entropy in the spectrograms). Acoustic signals can have structure above the basic signal units (elements), like in long repertoire songs or multi-syllable calls, in which elements are always broadcast as a sequences, often with consistent order and timing. It is then desirable to keep information about the relative position of elements in these sequences. However,‘by element’ extended selection tables discards some element sequence information. This can be overwritten using the argument `by.song`, which allows to keep in a single wave object all the elements belonging to the same ‘song’. In this case song refers to any grouping of sounds above the ‘element’ level.

The song of the Scale-throated Hermit (Phaethornis eurynome) will be used to show how this can be done. This song consists of a sequence of two elements, which are separated by short gaps:

Annotated spectrogram of Scale-throated Hermit songs. Vertical orange lines highlight songs while skyblue boxes show the frequency-time position of individual elements. The sound file can be found at https://xeno-canto.org/15607.

An example sound file with this species’ song can be downloaded as follows (the sound file can also be downloaded manually from this link):

    # load data
    data("sth_annotations")

    # download sound file from Xeno-Canto using catalog id
    out <-
      query_xc(qword = "nr:15607",
               download = TRUE,
               path = tempdir())

    # check file is found in temporary directory
    list.files(path = tempdir(), "mp3")

    character(0)

warbleR comes with an example data set containing annotations on this sound file, which can be loaded like this:

    # load  Scale-throated Hermit example annotations
    data("sth_annotations")

Note that these annotations contain an additional column called ‘song’, with the song ID labels for elements (rows) belonging to the same song:

    # print into the console
    head(sth_annotations)

sound.files | selec | channel | start | end | bottom.freq | top.freq | song | element
--- | --- | --- | --- | --- | --- | --- | --- | ---
Phaethornis-eurynome-15607.mp3 | 1 | 1 | 0.774 | 0.952 | 4.08 | 8.49 | 1 | a
Phaethornis-eurynome-15607.mp3 | 2 | 1 | 0.976 | 1.152 | 2.98 | 9.15 | 1 | b
Phaethornis-eurynome-15607.mp3 | 3 | 1 | 2.808 | 2.984 | 4.30 | 6.95 | 2 | a
Phaethornis-eurynome-15607.mp3 | 4 | 1 | 3.009 | 3.185 | 2.98 | 10.00 | 2 | b
Phaethornis-eurynome-15607.mp3 | 5 | 1 | 4.201 | 4.382 | 4.08 | 6.95 | 3 | a
Phaethornis-eurynome-15607.mp3 | 6 | 1 | 4.401 | 4.572 | 3.20 | 10.00 | 3 | b

These data (annotations + sound file) can be used to create a ‘by song’ extended selection table. To do this the name of the column containing the ‘song’ level labels must be supplied to the argument ‘by.song’:

    # create by song extended selection table
    bs_est <-
      selection_table(X = sth_annotations,
                      extended = TRUE,
                      by.song = "song",
                      path = tempdir())

In a ‘by song’ extended selection table there are as many wave objects as songs in our annotation data:

    length(attr(bs_est, "wave.objects")) == length(unique(paste(bs_est$sound.files, bs_est$song)))

    [1] TRUE

We can extract an entire wave object to check that two elements are actually included:

    # extract wave object
    wave_song1 <-
      read_sound_file(
        X = bs_est,
        index = 1,
        from = 0,
        to = Inf
      )

    # plot spectro
    seewave::spectro(
      wave_song1,
      wl = 150,
      grid = FALSE,
      scale = FALSE,
      ovlp = 90,
      palette = viridis::viridis,
      collevels = seq(-100, 0 , 5),
      flim = c(1, 12)
    )

Note that ‘by song’ extended selection tables can be converted into ‘by element’ tables using the function `by_element_est()`.

### Performance

The use of extended_selection_table objects can improve performance (in our case, measured as time). Here we use microbenchmark to compare the performance of `sig2noise()` and ggplot2 to plot the results. First, a selection table with 1000 selections is created simply by repeating the sample data frame several times and then is converted to an extended selection table:

    # create long selection table
    lng.selec.table <- do.call(rbind, replicate(10, lbh_selec_table,
                                                simplify = FALSE))

    # relabels selec
    lng.selec.table$selec <- 1:nrow(lng.selec.table)

    # create extended selection table
    lng_est <- selection_table(X = lng.selec.table,
                               pb = FALSE,
                               extended = TRUE)

    # load packages
    library(microbenchmark)
    library(ggplot2)

    # check performance
    mbmrk.snr <- microbenchmark(
      extended = sig2noise(lng_est,
                           mar = 0.05),
      regular = sig2noise(lng.selec.table,
                          mar = 0.05),
      times = 50
    )

    autoplot(mbmrk.snr) + ggtitle("sig2noise")

autodetec image example

The function runs much faster in the extended selection tables. Performance gain is likely to improve when longer recordings and data sets are used (that is, to compensate for computing overhead).

### Sharing acoustic data

This new object class allows to share complete data sets, including acoustic data. To do this we can make use of the RDS file format to save extended selection tables. These files can be easily shared with others, allowing to share a entire acoustic data set in a single file, something that can be tricky when dealing with acoustic data. For example, the following code downloads an extended selection table of inquiry calls from Spix’s disc-winged bats used in Araya-Salas et al (2020) (it can take a few minutes! Can also be manually downloaded from here):

    URL <- "https://figshare.com/ndownloader/files/21167052"

    options(timeout = max(300, getOption("timeout")))

    download.file(
      url = URL,
      destfile = file.path(tempdir(), "est_inquiry.RDS"),
      method = "auto"
    )

    est <- readRDS(file.path(tempdir(), "est_inquiry.RDS"))

    nrow(est)

    [1] 336

This data is ready to be used. For instance, here I create a multipanel graph with the spectrograms of the first 6 selections:

    par(mfrow = c(3, 2), mar = rep(0, 4))

    for (i in 1:6) {
      wv <- read_sound_file(
        X = est,
        index = i,
        from = 0.05,
        to = 0.17
      )

      spectro(
        wv,
        grid = FALSE,
        scale = FALSE,
        axisX = FALSE,
        axisY = FALSE,
        ovlp = 90,
        flim = c(10, 50),
        palette = viridis::viridis,
        collevels = seq(-100, 0 , 5)
        )
    }

We can also measured pairwise cross correlation (for simplicity only on the first 4 rows):

    xcorr_inquiry <- cross_correlation(est[1:4, ])

    xcorr_inquiry

| T2018-01-04_11-37-50_0000010.wav_1-1 | T2018-01-04_11-37-50_0000010.wav_10-1 | T2018-01-04_11-37-50_0000010.wav_11-1 | T2018-01-04_11-37-50_0000010.wav_12-1
--- | --- | --- | --- | ---
T2018-01-04_11-37-50_0000010.wav_1-1 | 1.000 | 0.522 | 0.535 | 0.594
T2018-01-04_11-37-50_0000010.wav_10-1 | 0.522 | 1.000 | 0.869 | 0.660
T2018-01-04_11-37-50_0000010.wav_11-1 | 0.535 | 0.869 | 1.000 | 0.833
T2018-01-04_11-37-50_0000010.wav_12-1 | 0.594 | 0.660 | 0.833 | 1.000

## 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

- Araya-Salas (2017), Rraven: connecting R and Raven bioacoustic software. R package version 1.0.2.
- Araya-Salas, M., Hernández-Pinsón, H. A., Rojas, N., & Chaverri, G. (2020). Ontogeny of an interactive call-and-response system in Spix’s disc-winged bats. Animal Behaviour, 166, 233-245.