warbleR is a package designed to streamline analysis of animal acoustic signals in R. This package allows users to collect open-access avian vocalizations data or input their own data into a workflow that facilitates spectrographic visualization and measurement of acoustic parameters. warbleR makes fundamental sound analysis tools from the R package seewave, as well as new tools not yet offered in the R environment, readily available for batch process analysis. The functions facilitate searching and downloading avian vocalizations from 'Xeno-Canto' http://www.xeno-canto.org/, creating maps of 'Xeno-Canto' recordings, converting .mp3 files to .wav files, checking .wav files, automatically detecting acoustic signals, selecting them manually, printing spectrograms of whole recordings or individual signals, measuring signal to noise ratio, cross-correlation and performing acoustic measurements.
The warbleR package offers three overarching categories of functions:
Obtaining avian vocalization data
Sound file management
Streamlined (bio)acoustic analysis in R
querxc: Download recordings and metadata from 'Xeno-Canto'
sim_songs: Simulate animal vocalizations
make.selection.table: Create 'selection.table' class objects
mp32wav: Convert several .mp3 files in working directory to .wav
format
checksels: Check whether selections can be read by subsequent functions
checkwavs: Check whether .wav files can be read by subsequent
functions and the minimum windows length ("wl" argument) that can be used
fixwavs: Fix .wav files to allow importing them into R
wavdur: Determine the duration of sound files
cut_sels: Cut selections from a selection table into individual sound files
rm_sil: Remove silence segments from wave files
consolidate: Consolidate sound files into a single folder
autodetec: Automatically detect start and
end of acoustic signals
manualoc: Interactive spectrographic view to measure start and
end of acoustic signals
autodetec: Automatic detection of acoustic signals based on ampltiude
seltailor: Interactive view of spectrograms to tailor start and end of selections
sig2noise: Measure signal-to-noise ratio across multiple files
trackfreqs: Create spectrograms to visualize frequency
measurements
filtersels: Filter selection data frames based on filtered image files
frange: Detect frequency range iteratively from signals in a selection table
frange.detec: Detect frequency range in a Wave object
specan: Measure acoustic parameters on selected acoustic
signals
xcorr: Pairwise cross-correlation of multiple signals
dfts: Extract the dominant frequency values across the signal as a time series
ffts: Extract the fundamental frequency values across the signal as a time series
sp.en.ts: Extract the spectral entropy values across the signal as a time series
dfDTW: Calculate acoustic dissimilarity using dynamic time warping
on dominant frequency contours
ffDTW: Calculate acoustic dissimilarity using dynamic time warping
on fundamental frequency contours
compare.methods: Produce graphs to visually assess performance of acoustic
distance measurements
coor.test: Assess statistical significance of singing coordination
ovlp_sels: Find selections that overlap in time within a given sound file
track_harm: Track harmonic frequency contour
xcmaps: Create maps to visualize the geographic spread of 'Xeno-Canto' recordings
catalog: Produce a vocalization catalog with spectrograms in and array with
several rows and columns
catalog2pdf: Combine catalog images to single pdf files
coor.graph: Creat graphs of coordinated singing
color.spectro: Highlight spectrogram regions
xcorr.graph: Pairwise cross-correlation of multiple signals
lspec: Produce spectrograms of whole recordings split into
multiple rows
lspec2pdf: Combine lspec images to single pdf files
specreator: Create spectrograms of manualoc selections
snrspecs: Create spectrograms to visualize margins over which
noise will be measured by sig2noise
License: GPL (>= 2)