warbleR (version 1.1.25)

xcorr: Time-frequency cross-correlation

Description

xcorr estimates the similarity of two sound waves by means of time-frequency cross-correlation

Usage

xcorr(X, wl = 512, bp = "pairwise.freq.range", ovlp = 70, dens = NULL, wn = 'hanning', 
cor.method = "pearson", parallel = 1, path = NULL, pb = TRUE, na.rm = FALSE,
 cor.mat = NULL, output = "cor.mat", compare.matrix = NULL, type = "spectrogram", 
 nbands = 40, method = 1)

Arguments

X

'selection_table', 'extended_selection_table' or data frame containing columns for sound files (sound.files), selection number (selec), and start and end time of signal (start and end). All selections must have the same sampling rate.

wl

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

bp

A numeric vector of length 2 for the lower and upper limits of a frequency bandpass filter (in kHz) or "pairwise.freq.range" (default) to indicate that values in lowest bottom.freq and highest top.freq columns for the signals involved in a pairwise comparison will be used as bandpass limits.

ovlp

Numeric vector of length 1 specifying % of overlap between two consecutive windows, as in spectro. Default is 70. High values of ovlp slow down the function but produce more accurate results.

dens

DEPRECATED.

wn

A character vector of length 1 specifying the window name as in ftwindow.

cor.method

A character vector of length 1 specifying the correlation method as in cor.

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.

pb

Logical argument to control progress bar. Default is TRUE.

na.rm

Logical. If TRUE all NAs produced when pairwise cross-correlations failed are removed from the results. This means that all selections with at least 1 cross-correlation that failed are excluded.

cor.mat

DEPRECATED. Use 'compare.matrix' instead.

output

Character vector of length 1 to determine if only the correlation matrix is returned ('cormat') or a list ('list') containing 1) the correlation matrix and 2) a data frame with correlation values at each sliding step for each comparison. The list, which is also of class 'xcorr.output', can be used to find detection peaks with find_peaks or to graphically explore detections using lspec.

compare.matrix

A character matrix with 2 columns indicating the selections to be compared (column 1 vs column 2). The columns must contained the ID of the selection, which is given by combining the 'sound.files' and 'selec' columns of 'X', separated by '-' (i.e. paste(X$sound.files, X$selec, sep = "-")). Default is NULL. If supplied only those comparisons will be calculated (as opposed to all pairwise comparisons as the default behavior) and the output will be a data frame composed of the supplied matrix and the correspondent cross-correlation values. Note that 'method' is automatically set to 2 (create spectrograms on the fly) when 'compare.matrix' is supplied but can be set back to 1.

type

A character vector of length 1 specifying the type of cross-correlation; either "spectrogram" (i.e. spectrographic cross-correlation using Fourier transform; internally using spectro; default) or "mfcc" (Mel cepstral coefficient cross-correlation; internally using melfcc).

nbands

Numeric vector of length 1 controlling the number of warped spectral bands to calculate when using type = "mfcc" (see melfcc). Default is 40.

method

Numeric vector of length 1 to control the method used to create spectrogram (or mfcc) matrices. Two option are available:

• 1: matrices are created first (keeping them internally as a list) and cross-correlation is calculated on a second step. Note that this method may require lots of memory if selection and or sound files are large.

• 2: matrices are created "on the fly" (i.e. at the same time that cross-correlation is calculated). More memory efficient but may require extracting the same matrix several times, which will affect performance. Note that when using this method the function does not check if sound files have the same sampling rate which if not, may produce an error.

Value

If output = "cor.mat" the function returns a matrix with the maximum (peak) correlation for each pairwise comparison (if 'compare.matrix' is not supplied) or the peak correlation for each comparison in the supplied 'compare.matrix'. Otherwise it will return a list that includes 1) a matrix with the maximum correlation for each pairwise comparison ('max.xcorr.matrix') and 2) a data frame with the correlation statistic for each "sliding" step ('scores').

Details

This function calculates the pairwise similarity of multiple signals by means of time-frequency cross-correlation. Spectrographic cross-correlation (SPCC, i.e. Fourier transform) and Mel frequency cepstral coefficients (mfcc) can be applied to create time-frequency representations of sound. This method "slides" the spectrogram of the sorthest selection over the longest one calculating a correlation of the amplitude values at each step. The function runs pairwise cross-correlations on several signals and returns a list including the correlation statistic for each "sliding" step as well as the maximum (peak) correlation for each pairwise comparison. To accomplish this the margins of the signals are expanded by half the duration of the signal both before and after the provided time coordinates. The correlation matrix could have NA's if some of the pairwise correlation did not work (common when sound files have been modified by band-pass filters).

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.

H. Khanna, S.L.L. Gaunt & D.A. McCallum (1997). Digital spectrographic cross-correlation: tests of sensitivity. Bioacoustics 7(3): 209-234

Lyon, R. H., & Ordubadi, A. (1982). Use of cepstra in acoustical signal analysis. Journal of Mechanical Design, 104(2), 303-306.

See Also

mfcc_stats, specan, df_DTW

Examples

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

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

# run cross correlation on spectrograms (SPCC)
xcor <- xcorr(X = lbh_selec_table, wl = 300, ovlp = 90, path = tempdir())

# run cross correlation on Mel cepstral coefficients (mfccs)
xcor <- xcorr(X = lbh_selec_table, wl = 300, ovlp = 90, path = tempdir(), type = "mfcc")
  
# using the 'compare.matrix' argument to specify pairwise comparisons
# create matrix with ID of signals to compare
cmp.mt <- cbind(
paste(lbh_selec_table$sound.files[1:10], lbh_selec_table$selec[1:10], sep = "-"), 
paste(lbh_selec_table$sound.files[2:11], lbh_selec_table$selec[2:11], sep = "-"))

# run cross-correlation on the selected pairwise comparisongs
xcor <- xcorr(X = lbh_selec_table, compare.matrix = cmp.mt, 
wl = 300, ovlp = 90, path = tempdir())
}
# }