R

Somehow measuring a bunch of spectral/temporal parameters and then reducing its dimensionality using principal component analysis has become the standard procedure when looking at variation in signal structure (i.e. measuring acoustic space), particularly in behavioral ecology and comparative bioacoustics. In most cases the approach is used without any kind of ground-truthing that can help validate the analysis. Given the complexity of animal acoustic signals, the approach could miss key signal features. [Read More]

The Rraven package is designed to facilitate the exchange of data between R and Raven sound analysis software (Cornell Lab of Ornithology). Raven provides very powerful tools for the analysis of (animal) sounds. R can simplify the automatization of complex routines of analyses. Furthermore, R packages as warbleR, seewave and monitoR (among others) provide additional methods of analysis, working as a perfect complement for those found in Raven. Hence, bridging these applications can largely expand the bioacoustician’s toolkit. [Read More]

Here I show how to use the dfDTW function in warbleR to compare acoustics signals using dynamic time warping (DTW). First load these packages (if not installed the code will install it): x<-c("vegan", "warbleR") A <- lapply(x, function(y) { if(!y %in% installed.packages()[,"Package"]) install.packages(y) require(y, character.only = T) }) and load example data from warbleR # optional, save it in a temporal folder # setwd(tempdir()) data(list = c( "Phae.long1", "Phae.long2","Phae.long3", "Phae.long4","selec.table")) writeWave(Phae. [Read More]

A [previous post]https://marce10.github.io/coded_bioacoustics/post/2017-03-17-creating_song_catalogs/) described the new function catalog. Here are a few updates on catalog based on suggestions from warbleR users. To be able to run the code you need warbleR 1.1.9 or higher, which hasn’t been released on CRAN and it’s only available in the most recent development version on github. It can be installed using the devtools package as follows # install devtools if is not yet installed if(! [Read More]

I got the following question about dynamic time warping on frequency contours: “what I am looking for is to use ffDTW on a file in which I have a column for the filename and then 20 pitch measurements for each of 10000 files (e.g. 10000 rows). Do you have suggestions?” There is a workaround in warbleR to do that: The function dfDTW() has the argument ts.df (for time series data frame) that allows to input your own frequency contours (or any other sequences of values taken along the signals). [Read More]

Animal vocalizations can be hierarchically structured: elements group together in syllables, syllables in songs, songs in bouts and so on. Many important biological patterns of vocal variation are better described at higher structural levels, so we are often interested in characterizing vocalizations at those levels. There are several tools in warbleR to explore and measure features above the element level. For simplicity, any level above ’elements’ will be refered to as ‘songs’ in this post as well as in the warbleR functions described here. [Read More]