| warbleR {warbleR} | R Documentation |
warbleR: A package to streamline bioacoustic analysis
Description
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.
Details
The main features of the package are:
The use of loops to apply tasks through acoustic signals referenced in a selection table
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)
Obtaining animal vocalization data
query_xc: Download recordings and/or metadata from 'Xeno-Canto'
sim_songs: Simulate animal vocalizations
Managing sound files
read_wave: Read wave files into 'wave' objects
read_sound_file: Read sound files into 'wave' objects
selection_table: Create 'selection_table' class objects
mp32wav: Convert several .mp3 files in working directory to .wav
format
check_sels: Check whether selections can be read by subsequent functions
check_wavs: Check whether .wav files can be read by subsequent
functions and the minimum windows length ("wl" argument) that can be used
fix_wavs: Fix .wav files so they can be read by other functions
split_wavs: Split .wav fies in several sound files
resample_est_waves: Resample wave objects in extended selection tables
duration_wavs: Determine the duration of sound files
cut_sels: Cut selections from a selection table into individual sound files
remove_silence: Remove silence segments from wave files
remove_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
Exploring/analyzing signal structure
auto_detec: Automatically detect start and
end of acoustic signals
tailor_sels: Interactive view of spectrograms to tailor start and end of selections
sig2noise: Measure signal-to-noise ratio across multiple files
track_freq_contour: Create spectrograms to visualize frequency
measurements
filter_sels: Filter selection data frames based on filtered image files
freq_range: Detect frequency range iteratively from signals in a selection table
freq_range_detec: Detect frequency range in a Wave object
spectro_analysis: Measure acoustic parameters on selected acoustic
signals
mfcc_stats: Calculate descriptive statistics on Mel-frequency cepstral coefficients
song_analysis: Measure acoustic parameters at higher levels of organization
cross_correlation: Pairwise cross-correlation of multiple signals
gaps: Measures gap duration
freq_ts: Extract frequency contours the signal as a time series
find_peaks: Find peaks in cross-correlation scores from cross_correlation
freq_DTW: Calculate acoustic dissimilarity using dynamic time warping
on frequency contours
wpd_features: Measure wavelet packet decomposition features
compare_methods: Produce graphs to visually assess performance of acoustic
distance measurements
test_coordination: Assess statistical significance of singing coordination
overlapping_sels: Find selections that overlap in time within a given sound file
track_harmonic: Track harmonic frequency contour
Graphical outputs
map_xc: 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
plot_coordination: Create graphs of coordinated singing
color_spectro: Highlight spectrogram regions
full_spectrograms: Produce spectrograms of whole recordings split into
multiple rows
full_spectrogram2pdf: Combine lspec images to single pdf files
spectrograms: Create spectrograms of selections
snr_spectrograms: Create spectrograms to visualize margins over which
noise will be measured by sig2noise
phylo_spectro: Add spectrograms onto phylogenetic trees
tweak_spectro: Visually inspect effect of different settings for creating (and improving) spectrograms
Author(s)
Marcelo Araya-Salas & Grace Smith Vidaurre
Maintainer: Marcelo Araya-Salas (marcelo.araya@ucr.ac.cr)
See Also
Useful links: