dfts extracts the dominant frequency values as a time series.
of signals selected by manualoc or autodetec.
dfts(X, wl = 512, length.out = 20, wn = "hanning", ovlp = 70, bp = c(0, 22), threshold = 15, img = TRUE, parallel = 1, path = NULL, img.suffix = "dfts", pb = TRUE, clip.edges = FALSE, leglab = "dfts", ...)ftwindow for more options.spectro. Default is 70.FALSE, image files are not produced. Default is TRUE.TRUE. Note that progress bar is only used
when parallel = 1.TRUE this edges will be excluded and signal contour will be calculated on the
remainging values. Default is FALSE.trackfreqs for customizing
graphical output.TRUE it also produces image files with the spectrograms of the signals listed in the
input data frame showing the location of the dominant frequencies
(see trackfreqs description for more details).
approx function to interpolate values between dominant frequency
measures. If there are no frequencies above the amplitude theshold at the begining or end
of the signals then NAs will be generated. On the other hand, if there are no frequencies
above the amplitude theshold in between signal segments in which amplitude was
detected then the values of this adjacent segments will be interpolated
to fill out the missing values (e.g. no NAs in between detected amplitude segments).
specreator for creating spectrograms from selections,
snrspecs for creating spectrograms to
optimize noise margins used in sig2noiseOther spectrogram.creators: dfDTW,
ffDTW, ffts,
snrspecs, sp.en.ts,
specreator, trackfreqs
## Not run:
# # set the temp directory
# setwd(tempdir())
#
# #load data
# data(list = c("Phae.long1", "Phae.long2","selec.table"))
# writeWave(Phae.long2, "Phae.long2.wav") #save sound files
# writeWave(Phae.long1, "Phae.long1.wav")
#
# # run function
# dfts(X = selec.table, length.out = 30, flim = c(1, 12), bp = c(2, 9), wl = 300)
#
# ## End(Not run)
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