warbleR (version 1.1.17)

multi_DTW: A wrapper on dtwDist for comparing multivariate contours

Description

multi_DTW is a wrapper on dtwDist that simplify applying dynamic time warping on multivariate contours.

Usage

multi_DTW(ts.df1 = NULL, ts.df2 = NULL, pb = TRUE,  parallel = 1, 
window.type = "none", open.end = FALSE, scale = FALSE, dist.mat = TRUE, ...)

Arguments

ts.df1

Optional. Data frame with frequency contour time series of signals to be compared.

ts.df2

Optional. Data frame with frequency contour time series of signals to be compared.

pb

Logical argument to control progress bar. Default is TRUE. Note that progress bar is only used when parallel = 1.

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). Not availble in Windows OS.

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 dominant 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.

dist.mat

Logical controlling whether a distance matrix (TRUE, default) or a tabular data frame (FALSE) is returned.

...

Additional arguments to be passed to trackfreqs 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

specreator for creating spectrograms from selections, snrspecs for creating spectrograms to optimize noise margins used in sig2noise and dfts, ffts, ffDTW for frequency contour overlaid spectrograms. blog post on DTW similarity

Other spectrogram creators: color.spectro, dfDTW, dfts, ffDTW, ffts, phylo_spectro, snrspecs, sp.en.ts, specreator, trackfreqs

Examples

# NOT RUN {
#load data
data(list = c("Phae.long1", "Phae.long2", "Phae.long3", "Phae.long4", "lbh_selec_table"))

writeWave(Phae.long1, file.path(tempdir(), "Phae.long1.wav")) #save sound files 
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"))

# measure
df <- df_ts(X = lbh_selec_table, threshold = 10, img = FALSE, path = tempdir())
se <- se_ts(X = lbh_selec_table, threshold = 10, img = FALSE, path = tempdir())

# run function 
multi_DTW(df, se)
# }
# NOT RUN {
# }