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Acoustic dissimilarity using dynamic time warping on dominant frequency contours

Source: R/freq_DTW.R
freq_DTW.Rd

freq_DTW calculates acoustic dissimilarity of frequency contours using dynamic time warping. Internally it applies the dtwDist function from the dtw package.

Usage

freq_DTW(X = NULL, type = "dominant", wl = 512, wl.freq = 512, length.out = 20,
wn = "hanning", ovlp = 70, bp = NULL, threshold = 15, threshold.time = NULL,
threshold.freq = NULL, img = TRUE, parallel = 1, path = NULL, ts.df = NULL,
img.suffix = "dfDTW", pb = TRUE, clip.edges = TRUE, window.type = "none",
open.end = FALSE, scale = FALSE, frange.detec = FALSE,  fsmooth = 0.1,
adjust.wl = TRUE, ...)

Arguments

X

object of class 'selection_table', 'extended_selection_table' or data frame containing columns for sound file name (sound.files), selection number (selec), and start and end time of signal (start and end). The output of auto_detec can be used as the input data frame.

type

Character string to determine the type of contour to be detected. Three options are available, "dominant" (default), "fundamental" and "entropy".

wl

A numeric vector of length 1 specifying the window length of the spectrogram, default is 512.

wl.freq

A numeric vector of length 1 specifying the window length of the spectrogram for measurements on the frequency spectrum. Default is 512. Higher values would provide more accurate measurements.

length.out

A numeric vector of length 1 giving the number of measurements of frequency desired (the length of the time series).

wn

Character vector of length 1 specifying window name. Default is "hanning". See function ftwindow for more options.

ovlp

Numeric vector of length 1 specifying % of overlap between two consecutive windows, as in spectro. Default is 70.

bp

A numeric vector of length 2 for the lower and upper limits of a frequency bandpass filter (in kHz). Default is NULL.

threshold

amplitude threshold (%) for frequency detection. Default is 15.

threshold.time

amplitude threshold (%) for the time domain. Use for frequency detection. If NULL (default) then the 'threshold' value is used.

threshold.freq

amplitude threshold (%) for the frequency domain. Use for frequency range detection from the spectrum (see 'frange.detec'). If NULL (default) then the 'threshold' value is used.

img

Logical argument. If FALSE, image files are not produced. Default is TRUE.

parallel

Numeric. Controls whether parallel computing is applied. It specifies the number of cores to be used. Default is 1 (i.e. no parallel computing).

path

Character string containing the directory path where the sound files are located. If NULL (default) then the current working directory is used.

ts.df

Optional. Data frame with frequency contour time series of signals to be compared. If provided "X" is ignored.

img.suffix

A character vector of length 1 with a suffix (label) to add at the end of the names of image files. Default is NULL.

pb

Logical argument to control progress bar. Default is TRUE.

clip.edges

Logical argument to control whether edges (start or end of signal) in which amplitude values above the threshold were not detected will be removed. If TRUE (default) this edges will be excluded and contours will be calculated on the remaining values. Note that DTW cannot be applied if missing values (e.i. when amplitude is not detected).

window.type

dtw windowing control parameter. Character: "none", "itakura", or a function (see dtw).

open.end

dtw control parameter. Performs open-ended alignments (see dtw).

scale

Logical. If TRUE frequency values are z-transformed using the scale function, which "ignores" differences in absolute frequencies between the signals in order to focus the comparison in the frequency contour, regardless of the pitch of signals. Default is TRUE.

frange.detec

DEPRECATED.

fsmooth

A numeric vector of length 1 to smooth the frequency spectrum with a mean sliding window (in kHz) used for frequency range detection (when frange.detec = TRUE). This help to average amplitude "hills" to minimize the effect of amplitude modulation. Default is 0.1.

adjust.wl

Logical. If TRUE 'wl' (window length) is reset to be lower than the number of samples in a selection if the number of samples is less than 'wl'. Default is TRUE.

...

Additional arguments to be passed to track_freq_contour for customizing graphical output.

Value

A matrix with the pairwise dissimilarity values. If img is FALSE it also produces image files with the spectrograms of the signals listed in the input data frame showing the location of the dominant frequencies.

Details

This function extracts the dominant frequency values as a time series and then calculates the pairwise acoustic dissimilarity using dynamic time warping. The function uses the approx function to interpolate values between dominant frequency measures. If 'img' is TRUE the function also produces image files with the spectrograms of the signals listed in the input data frame showing the location of the dominant frequencies.

References

Araya-Salas, M., & Smith-Vidaurre, G. (2017). warbleR: An R package to streamline analysis of animal acoustic signals. Methods in Ecology and Evolution, 8(2), 184-191.

See also

spectrograms for creating spectrograms from selections, snr_spectrograms for creating spectrograms to optimize noise margins used in sig2noise and freq_ts, freq_ts, for frequency contour overlaid spectrograms.

Other spectrogram creators: color_spectro(), multi_DTW(), phylo_spectro(), snr_spectrograms(), spectrograms(), track_freq_contour()

Author

Marcelo Araya-Salas (marcelo.araya@ucr.ac.cr)

Examples

{
  # load data
  data(list = c("Phae.long1", "Phae.long2", "lbh_selec_table"))
  writeWave(Phae.long2, file.path(tempdir(), "Phae.long2.wav")) # save sound files
  writeWave(Phae.long1, file.path(tempdir(), "Phae.long1.wav"))

  # dominant frequency
  freq_DTW(lbh_selec_table,
    length.out = 30, flim = c(1, 12), bp = c(2, 9),
    wl = 300, path = tempdir()
  )

  # fundamental frequency
  freq_DTW(lbh_selec_table,
    type = "fundamental", length.out = 30, flim = c(1, 12),
    bp = c(2, 9), wl = 300, path = tempdir()
  )
}
#> measuring dominant frequency contours (step 1 of 2): 
#> 
#> Creating spectrograms overlaid with fundamental frequency measurements:
#> calculating DTW distances (step 2 of 2, no progress bar):
#> measuring dominant frequency contours (step 1 of 2): 
#> 
#> Creating spectrograms overlaid with fundamental frequency measurements:
#> calculating DTW distances (step 2 of 2, no progress bar):
#>                  Phae.long1.wav-1 Phae.long1.wav-2 Phae.long1.wav-3
#> Phae.long1.wav-1           0.0000          20.2601          20.2518
#> Phae.long1.wav-2          20.2601           0.0000          13.2831
#> Phae.long1.wav-3          20.2518          13.2831           0.0000
#> Phae.long2.wav-1          15.4428          89.3132          83.3434
#> Phae.long2.wav-2          17.7108          84.4165          75.8621
#> Phae.long3.wav-1          35.7880         105.3692          93.4466
#> Phae.long3.wav-2          12.7952          89.8322          81.4853
#> Phae.long3.wav-3          33.2880          99.5629          86.9659
#> Phae.long4.wav-1          20.6536          23.4967          31.0400
#> Phae.long4.wav-2          20.3148          22.7303          29.2321
#> Phae.long4.wav-3          19.8954          21.8002          28.3667
#>                  Phae.long2.wav-1 Phae.long2.wav-2 Phae.long3.wav-1
#> Phae.long1.wav-1          15.4428          17.7108          35.7880
#> Phae.long1.wav-2          89.3132          84.4165         105.3692
#> Phae.long1.wav-3          83.3434          75.8621          93.4466
#> Phae.long2.wav-1           0.0000          11.0944           9.0332
#> Phae.long2.wav-2          11.0944           0.0000          25.5835
#> Phae.long3.wav-1           9.0332          25.5835           0.0000
#> Phae.long3.wav-2           4.7227          12.1370           4.6336
#> Phae.long3.wav-3           8.2250          16.2625           3.5236
#> Phae.long4.wav-1          49.0280          42.5728          64.5342
#> Phae.long4.wav-2          50.2424          42.5583          63.3510
#> Phae.long4.wav-3          50.7279          41.8545          63.9772
#>                  Phae.long3.wav-2 Phae.long3.wav-3 Phae.long4.wav-1
#> Phae.long1.wav-1          12.7952          33.2880          20.6536
#> Phae.long1.wav-2          89.8322          99.5629          23.4967
#> Phae.long1.wav-3          81.4853          86.9659          31.0400
#> Phae.long2.wav-1           4.7227           8.2250          49.0280
#> Phae.long2.wav-2          12.1370          16.2625          42.5728
#> Phae.long3.wav-1           4.6336           3.5236          64.5342
#> Phae.long3.wav-2           0.0000           8.1572          53.3453
#> Phae.long3.wav-3           8.1572           0.0000          64.8988
#> Phae.long4.wav-1          53.3453          64.8988           0.0000
#> Phae.long4.wav-2          53.7884          64.0806           1.8670
#> Phae.long4.wav-3          53.8636          65.3580           2.8345
#>                  Phae.long4.wav-2 Phae.long4.wav-3
#> Phae.long1.wav-1          20.3148          19.8954
#> Phae.long1.wav-2          22.7303          21.8002
#> Phae.long1.wav-3          29.2321          28.3667
#> Phae.long2.wav-1          50.2424          50.7279
#> Phae.long2.wav-2          42.5583          41.8545
#> Phae.long3.wav-1          63.3510          63.9772
#> Phae.long3.wav-2          53.7884          53.8636
#> Phae.long3.wav-3          64.0806          65.3580
#> Phae.long4.wav-1           1.8670           2.8345
#> Phae.long4.wav-2           0.0000           1.4648
#> Phae.long4.wav-3           1.4648           0.0000