rm_sil
rm_sil(path = NULL, min.sil.dur = 2, img = TRUE, it = "jpeg", flim = c(0, 12),
flist = NULL, parallel = 1, pb = TRUE)
Character string containing the directory path where the sound files are located.
If NULL
(default) then the current working directory is used.
Numeric. Controls the minimum duration of silence segments that would be removed.
Logical argument. If FALSE
, image files are not produced. Default is TRUE
.
A character vector of length 1 giving the image type to be used. Currently only "tiff" and "jpeg" are admitted. Default is "jpeg".
A numeric vector of length 2 indicating the highest and lowest
frequency limits (kHz) of the spectrogram as in
spectro
. Default is c(0,12). Ignored if `img = FALSE`.
character vector or factor indicating the subset of files that will be analyzed. If not provided then all wave files in the working directory (or path) will be processed.
Numeric. Controls whether parallel computing is applied. It specifies the number of cores to be used. Default is 1 (i.e. no parallel computing).
Logical argument to control progress bar and messages. Default is TRUE
.
Sound files for which silence segments have been removed are saved in the new folder "removed_silence_files". If `img = TRUE` then spectrogram images highlighting the silence segments that were removed are also saved.
The function removes silence segments (i.e. segments with very low amplitude values) from wave files.
# NOT RUN {
{
# Set temporary working directory
# setwd(tempdir())
# save sound file examples
data(list = c("Phae.long1", "Phae.long2","selec.table"))
sil <- silence(samp.rate = 22500, duration = 3, xunit = "time")
wv1 <- pastew(pastew(Phae.long1, sil, f = 22500, output = "Wave"),
Phae.long2, f = 22500, output = "Wave")
#check silence in between amplitude peaks
env(wv1)
#save wave file
writeWave(object = wv1, filename = "wv1.wav", extensible = FALSE)
#remove silence
rm_sil(flist = "wv1.wav")
# OR this is tempdir was used instead
# rm_sil(path = tempdir(), flist = "wv1.wav")
#check this floder
getwd()
}
# }
Run the code above in your browser using DataLab