implement call detection and create pdfs

warbleR_xeno-canto/warbleR_xeno-canto.r

@@ -67,4 +67,82 @@ write.csv(Phae.lon.LS, "Phae_lon.LS.csv", row.names = FALSE)
 # to speed up downstream analyses in the vignette series
 mp32wav(samp.rate = 22.05)
 # Use checkwavs to see if wav files can be read
-check_wavs()
+check_wavs()
+
+##
+# Make long spectrograms of whole recordings
+##
+
+# Create a vector of all the recordings in the directory
+wavs <- list.files(pattern = "wav$")
+# Print this object to see all sound files
+wavs
+# How long are these files? this will determine number of pages returned by full_spectrograms
+duration_wavs(wavs)
+# ovlp = 10 to speed up function
+# tiff image files are better quality and are faster to produce
+full_spectrograms(flist = wavs, ovlp = 10, it = "tiff")
+# We can zoom in on the frequency axis by changing flim,
+# the number of seconds per row, and number of rows
+full_spectrograms(flist = wavs, flim = c(2, 10), sxrow = 6, rows = 15, ovlp = 10, it = "tiff")
+
+##
+# Once satisfied with the argument settings we can make long spectrograms for all the sound files.
+##
+
+# Make long spectrograms for the xeno-canto sound files
+full_spectrograms(flim = c(2, 10), ovlp = 10, sxrow = 6, rows = 15, it = "jpeg", flist = wavs)
+# Concatenate full_spectrograms image files into a single PDF per recording
+# full_spectrograms images must be jpegs to do this
+full_spectrogram2pdf(keep.img = FALSE, overwrite = TRUE)
+
+##
+# Automatically detect signals with auto_detect
+##
+
+# Select a subset of sound files
+# Reinitialize the wav object
+wavs <- list.files(pattern = ".wav$", ignore.case = TRUE)
+# Set a seed so we all have the same results
+set.seed(1)
+sub <- wavs[sample(1:length(wavs), 3)]
+
+##
+# Run auto_detec() on subset of recordings
+##
+
+# Once we’re satisfied with the detection, we can run the auto_detec on all the 
+# recordings, removing the argument flist (so auto_detec runs over all wav files
+# in the working directory). We will also save the temporal output in an object.
+# Once we’re satisfied with the detection, we can run the auto_detec on all the 
+# recordings, removing the argument flist (so auto_detec runs over all wav files
+# in the working directory). We will also save the temporal output in an object.
+Phae.ad <- auto_detec(
+  path = wd,
+  threshold = 20, # amplitude threshold in %
+  ssmooth = 900, # amplitude envelope with sum smooth
+  bp = c(2, 10), # bandpass filter (between 2 and 10 kHz)
+  wl = 300, # window for ffilter bandpass
+  parallel = 6*2 # how many cores shall be used in parallel (*2 due to hyper threading)
+)
+
+# Let’s look at the number of selections per sound file
+table(Phae.ad$sound.files)
+
+# create an image with all detections
+full_spectrograms(flim = c(2, 10), 
+                  ovlp = 10, 
+                  sxrow = 6, 
+                  rows = 15,
+                  it = "jpeg",
+                  flist = wavs,
+                  X = auto_detec(
+                    path = wd,
+                    threshold = 20,
+                    ssmooth = 900,
+                    bp = c(2, 10),
+                    wl = 300,
+                    parallel = 6*2))
+
+# combine the image into a single pdf per species like before
+full_spectrogram2pdf(keep.img = FALSE, overwrite = TRUE)