spectrum_correlation
measures frequency spectrum correlation of sounds referenced in an extended selection table.
Usage
spectrum_correlation(
X,
cores = getOption("mc.cores", 1),
pb = getOption("pb", TRUE),
cor.method = c("pearson", "spearman", "kendall"),
spec.smooth = getOption("spec.smooth", 5),
hop.size = getOption("hop.size", 11.6),
wl = getOption("wl", NULL),
ovlp = getOption("ovlp", 70),
path = getOption("sound.files.path", "."),
n.bins = 100
)
Arguments
- X
The output of
set_reference_sounds
which is an object of class 'data.frame', 'selection_table' or 'extended_selection_table' (the last 2 classes are created by the functionselection_table
from the warbleR package) with the reference to the test sounds . Must contain the following columns: 1) "sound.files": name of the .wav files, 2) "selec": unique selection identifier (within a sound file), 3) "start": start time and 4) "end": end time of selections, 5) "bottom.freq": low frequency for bandpass, 6) "top.freq": high frequency for bandpass, 7) "sound.id": ID of sounds used to identify counterparts across distances and 8) "reference": identity of sounds to be used as reference for each test sound (row). Seeset_reference_sounds
for more details on the structure of 'X'.- cores
Numeric vector of length 1. Controls whether parallel computing is applied by specifying the number of cores to be used. Default is 1 (i.e. no parallel computing). Can be set globally for the current R session via the "mc.cores" option (see
options
).- pb
Logical argument to control if progress bar is shown. Default is
TRUE
. Can be set globally for the current R session via the "pb" option (seeoptions
).- cor.method
Character string indicating the correlation coefficient to be applied ("pearson", "spearman", or "kendall", see
cor
).- spec.smooth
Numeric vector of length 1 determining the length of the sliding window used for a sum smooth for power spectrum calculation (in kHz). Default is 5.
- hop.size
A numeric vector of length 1 specifying the time window duration (in ms). Default is 11.6 ms, which is equivalent to 512 wl for a 44.1 kHz sampling rate. Ignored if 'wl' is supplied. Can be set globally for the current R session via the "hop.size" option (see
options
).- wl
a vector with a single even integer number specifying the window length of the spectrogram, default is
NULL
. If supplied, 'hop.size' is ignored. Odd integers will be rounded up to the nearest even number. Can be set globally for the current R session via the "wl" option (seeoptions
).- ovlp
Numeric vector of length 1 specifying the percentage of overlap between two consecutive windows, as in
spectro
. Default is 70. Can be set globally for the current R session via the "ovlp" option (seeoptions
).- path
Character string containing the directory path where the sound files are found. Only needed when 'X' is not an extended selection table. If not supplied the current working directory is used. Can be set globally for the current R session via the "sound.files.path" option (see
options
).- n.bins
Numeric vector of length 1 specifying the number of frequency bins to use for representing power spectra. Default is 100. If null the raw power spectrum is used (note that this can result in high RAM memory usage for large data sets). Power spectrum values are interpolated using
approx
.
Value
Object 'X' with an additional column, 'spectrum.correlation', containing the computed frequency spectrum correlation coefficients.
Details
spectral correlation measures the similarity of two sounds in the frequency domain. The function measures the spectral correlation coefficients of sounds in which a reference playback has been re-recorded at increasing distances. Values range from 1 (identical frequency spectrum, i.e. no degradation) to 0. The 'sound.id' column must be used to indicate the function to only compare sounds belonging to the same category (e.g. song-types). The function will then compare each sound to the corresponding reference sound. Two methods for computing spectral correlation are provided (see 'method' argument). The function uses meanspec
internally to compute power spectra. Use spectrum_blur_ratio
to extract raw spectra values. NA is returned if at least one the power spectra cannot be computed.
References
Araya-Salas M., E. Grabarczyk, M. Quiroz-Oliva, A. Garcia-Rodriguez, A. Rico-Guevara. (2023), baRulho: an R package to quantify degradation in animal acoustic signals .bioRxiv 2023.11.22.568305.Apol, C.A., Sturdy, C.B. & Proppe, D.S. (2017). Seasonal variability in habitat structure may have shaped acoustic signals and repertoires in the black-capped and boreal chickadees. Evol Ecol. 32:57-74.
See also
envelope_correlation
, spectrum_blur_ratio
Other quantify degradation:
blur_ratio()
,
detection_distance()
,
envelope_correlation()
,
plot_blur_ratio()
,
plot_degradation()
,
set_reference_sounds()
,
signal_to_noise_ratio()
,
spcc()
,
spectrum_blur_ratio()
,
tail_to_signal_ratio()
Author
Marcelo Araya-Salas (marcelo.araya@ucr.ac.cr)
Examples
{
# load example data
data("test_sounds_est")
# method 1
# add reference column
Y <- set_reference_sounds(X = test_sounds_est)
# run spectrum correlation
spectrum_correlation(X = Y)
# method 2
Y <- set_reference_sounds(X = test_sounds_est, method = 2)
# spectrum_correlation(X = Y)
}