wpd_features: Measure wavelet packet decomposition features (EXPERIMENTAL)

In maRce10/warbleR: Streamline Bioacoustic Analysis

Measure wavelet packet decomposition features (EXPERIMENTAL)

Description

wpd_features Measure wavelet packet decomposition features.

Usage

wpd_features(X, normalize = TRUE, threshold1 = 6,
threshold2 = 0.5, path = NULL, pb = TRUE, parallel = 1)

Arguments

X	object of class 'selection_table', 'extended_selection_table' or data frame with the following columns: 1) "sound.files": name of the sound 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.
normalize	Logical to determine if features are normalized by signal duration.
threshold1	Threshold (%) for wavelet coefficient detection. Equivalent to denominator of equation 6 in Selin et al (2007). Must be a value between 0 and 1.
threshold2	Threshold for width detection. Equivalent to threshold 2 (th2) in equation 7 in Selin et al (2007).
path	Character string containing the directory path where the sound files are located. If NULL (default) then the current working directory is used.
pb	Logical argument to control progress bar and messages. 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).

Details

Measures wavelet packet decomposition features. STILL UNDER DEVELOPMENT. USE IT UNDER YOUR OWN RISK.

Value

A data frame with rows for each of the selections in 'X' in addition to four wavelet packet decomposition features: max.energy, position, spread and width.

Author(s)

Marcelo Araya-Salas ([e-mail])

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.

Selin A., J. Turunen, and J. T. Tanttu, 2007. Wavelets in recognition of bird sounds. EURASIP Journal on Advances in Signal Processing.

See Also

mfcc_stats, mfcc_stats

Examples

{
  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, path = tempdir())
}

maRce10/warbleR documentation built on April 8, 2024, 11:08 p.m.