
wpd_features
Measure wavelet packet decomposition features.
wpd_features(X, normalize = TRUE, threshold1 = 6,
threshold2 = 0.5, path = NULL, pb = TRUE, parallel = 1)
object of class 'selection_table', 'extended_selection_table' or data frame with the following columns: 1) "sound.files": name of the .wav
files, 2) "sel": number of the selections, 3) "start": start time of selections, 4) "end":
end time of selections. The output of auto_detec
can
also be used as the input data frame.
Logical to determine if features are normalized by signal duration.
Threshold (%) for wavelet coefficient detection. Equivalent to denominator of equation 6 in Selin et al (2007). Must be a value between 0 and 1.
Threshold for width detection. Equivalent to threshold 2 (th2) in equation 7 in Selin et al (2007).
Character string containing the directory path where the sound files are located.
If NULL
(default) then the current working directory is used.
Logical argument to control progress bar and messages. Default is TRUE
.
Numeric. Controls whether parallel computing is applied. It specifies the number of cores to be used. Default is 1 (i.e. no parallel computing).
Measures wavelet packet decomposition features. STILL IN DEVELOPMENT. USE IT UNDER YOUR OWN RISK.
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.
Selin A., J. Turunen, and J. T. Tanttu, 2007. Wavelets in recognition of bird sounds. EURASIP Journal on Advances in Signal Processing.
# NOT RUN {
{
data(list = c("Phae.long1", "Phae.long2", "lbh_selec_table"))
writeWave(Phae.long1, file.path(tempdir(), "Phae.long1.wav"))
writeWave(Phae.long2, file.path(tempdir(), "Phae.long2.wav"))
# not normalize
wpd_features(lbh_selec_table[1:5, ], threshold2 = 0.3, nor = FALSE)
}
# }
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