Check out the new R package dynaSpec for creating dynamic spectrograms!

The advantage of R over most common sound analysis software (e.g. Raven, SAP, Avisoft) is its higher flexibility, which allows the implementation of custom made analyses that better fit the research questions and the characteristics of the vocalizations. However, this requires some proficiency in R coding that may dissuade beginners. Hopefully the codes I make available here will encourage bioacousticians to take advantage of this powerful tool.

Most of the tools I have developed for acoustic analysis are now available in the R packages warbleR, Rraven, baRulho and dynaSpec. I will also post R scripts to detail the usage of new addtions to these packages. Check out the article describing warbleR. Please cite this paper when using any of the package functions.

Sound analysis in R has been made possible owing to the awesome package seewave. Take a look at seewave’s website to learn more about its different tools. I will also post code containing functions from the packages monitoR, soundgen, bioacoustics and tuneR.


Automatic signal detection: a case study

Some recent additions to warbleR aim to simplify the automatic detection of signals. The current post details these additions with a study case detecting inquiry calls of Spix’s disc-winged bats (Thyroptera tricolor). [Read More]

dynaSpec: dynamic spectrograms in R

The R package dynaSpec can now be installed from github. This is a set of tools to generate dynamic spectrogram visualizations in video format. It is still on the making and new visualizations will be available soon. FFMPEG must be installed in order for this package to work. [Read More]

Signal detection with cross-correlation using warbleR

warbleR (v1.1.24) now includes functions to detect signals using cross-correlation similar to those in the package monitoR. There is already a blog post on cross-correlation detection using monitoR. In this post I show how to do that with warbleR and compare its performance against that from monitoR. [Read More]

Evaluating group acoustic signatures using cross-correlation

Social learning is often diagnosed by mapping the geographic variation of behavior. Behavioral variation at a small geographical scale that shows both sharp differences among localities and consistency within localities is indicative of social learning of local traditions. This pattern translates into a pretty straightforward statistical hypothesis: the behavior is... [Read More]

Working with higher structural levels in vocal signals

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... [Read More]

Spectrograms on trees

This post describes the new warbleR function phylo_spectro. The function adds spectrograms of sounds annotated in a selection table (‘X argument) onto the tips of a tree (of class ‘phylo’). The ‘tip.label’ column in ‘X’ is used to match spectrograms and tree tips. The function uses internally the plot.phylo function... [Read More]