warbleR is intended to facilitate the analysis of the structure of animal acoustic signals in R. Users can collect open-access avian recordings or enter their own data into a workflow that facilitates spectrographic visualization and measurement of acoustic parameters. warbleR makes use of the fundamental sound analysis tools of the seewave package, and offers new tools for acoustic structure analysis. These tools are available for batch analysis of acoustic signals.
querxc: Download recordings and/or metadata from 'Xeno-Canto'
sim_songs: Simulate animal vocalizations
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 so they can be read by other functions
resample_est: Resample wave objects in extended selection tables
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
rm_channels: Remove channels in wave files
consolidate: Consolidate sound files into a single folder
selection_table: Create double-checked and self-contained selection tables
fix_extended_selection_table: Fix attributes of extended selection tables
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
mfcc_stats: Calculate descriptive statistics on Mel-frequency cepstral coefficients
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
phylo_spectro: Add spectrograms onto phylogenetic trees
The main features of the package are:
The use of loops to apply tasks through acoustic signals referenced in a selection box
The production of images in the working folder with spectrograms that allow to organize data and verify acoustic analyzes
The package offers functions to:
Explore and download Xeno Canto recordings
Explore, organize and manipulate multiple sound files
Detect signals automatically (in frequency and time)
Create spectrograms of complete recordings or individual signals
Run different measures of acoustic signal structure
Evaluate the performance of measurement methods
Catalog signals
Characterize different structural levels in acoustic signals
Statistical analysis of duet coordination
Consolidate databases and annotation tables
Most of the functions allow the parallelization of tasks, which distributes the tasks among several processors to improve computational efficiency. Tools to evaluate the performance of the analysis at each step are also available. In addition, warbleR satisfies the need for rigorous open source bioacoustic analysis, which facilitates opportunities for use in research and innovation of additional custom analyzes.
The warbleR package offers three overarching categories of functions:
License: GPL (>= 2)