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 sig2noise
Other 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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