fix some typos
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@@ -111,12 +111,8 @@ sub <- wavs[sample(1:length(wavs), 3)]
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# Run auto_detec() on subset of recordings
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# Run auto_detec() on subset of recordings
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##
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##
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# Once we’re satisfied with the detection, we can run the auto_detec on all the
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# play around with the auto detection, setting the values accordingly.
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# recordings, removing the argument flist (so auto_detec runs over all wav files
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# Phaethornis longirostris songs have frequencies between 2 and 10 kHz and durations between 0.05 and 0.5 s.
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# in the working directory). We will also save the temporal output in an object.
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# Once we’re satisfied with the detection, we can run the auto_detec on all the
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# recordings, removing the argument flist (so auto_detec runs over all wav files
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# in the working directory). We will also save the temporal output in an object.
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Phae.ad <- auto_detec(
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Phae.ad <- auto_detec(
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path = wd,
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path = wd,
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threshold = 20, # amplitude threshold in %
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threshold = 20, # amplitude threshold in %
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@@ -126,11 +122,12 @@ Phae.ad <- auto_detec(
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parallel = 6*2 # how many cores shall be used in parallel (*2 due to hyper threading)
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parallel = 6*2 # how many cores shall be used in parallel (*2 due to hyper threading)
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)
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)
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# Let’s look at the number of selections per sound file
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# Let's look at the number of selections per sound file
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table(Phae.ad$sound.files)
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table(Phae.ad$sound.files)
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# create an image with all detections
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# When we're done, create an image with all detections using full spectrograms
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full_spectrograms(flim = c(2, 10),
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full_spectrograms(
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flim = c(2, 10),
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ovlp = 10,
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ovlp = 10,
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sxrow = 6,
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sxrow = 6,
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rows = 15,
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rows = 15,
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@@ -142,7 +139,8 @@ full_spectrograms(flim = c(2, 10),
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ssmooth = 900,
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ssmooth = 900,
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bp = c(2, 10),
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bp = c(2, 10),
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wl = 300,
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wl = 300,
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parallel = 6*2))
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parallel = 6*2)
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)
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# combine the image into a single pdf per species like before
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# combine the image into a single pdf per species like before
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full_spectrogram2pdf(keep.img = FALSE, overwrite = TRUE)
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full_spectrogram2pdf(keep.img = FALSE, overwrite = TRUE)
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