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0.1 Before the course starts

• Make sure you install all the software we will be using as detailed in here

 

Refresh basic R concepts R basics

• Object types in R
• Subsetting
• Style matters
• R documentation

 

 

1 Day 1 (Video)

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1.1 Aditional resources

1.1.1 Readings

• Alston, J. M., & Rick, J. A. (2021). A beginner’s guide to conducting reproducible research. Bulletin of the Ecological Society of America, 102(2), 1-14.
• Culina, A., van den Berg, I., Evans, S., & Sánchez-Tójar, A. (2020). Low availability of code in ecology: A call for urgent action. PLoS Biology, 18(7), e3000763.
• Köhler, J., Jansen, M., Rodríguez, A., Kok, P. J. R., Toledo, L. F., Emmrich, M., … & Vences, M. (2017). The use of bioacoustics in anuran taxonomy: theory, terminology, methods and recommendations for best practice. Zootaxa, 4251(1), 1-124. (at least the first 28 pages)

1.1.2 Videos

• Introduction to digital audio

• Digital audio artifacts: (Video 1, Video 2)

 

Introduction Introduction

• How animal acoustic signals look like?
• Analytical workflow in bioacoustics research
• Advantages of programming
• Course outline

 

What is sound? Sound

• Create a Rstudio project for the course

• Download this folder into the course project directory

• Sound as a time series
• Sound as a digital object
• Acoustic data in R
• ‘wave’ object structure
• ‘wave’ object manipulations
• additional formats

 

1.2 Homework

1. Use the function query_xenocanto() from the suwo package to check the availability of recordings for a species of interest in Xeno-Canto. This repository offers recordings for several taxonomic groups. Take a look at this link for a list of species by taxonomic group. Check this brief tutorial on how to get the recordings with suwo.

2. Subset the data frame returned by the function to get a subset of subspecies/populations or recordings from a specific country and for certain vocalization type (using base R subsetting tools)

3. Download the associated recordings using download_media()

4. Explore the recordings with any spectrogram creating GUI program (e.g. Raven, Audacity)

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2 Day 2 (Video)

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2.1 Additional resources

2.1.1 Raven tutorials

• Introduction to the Raven Pro Interface
• Introduction to selections and measurements
• Saving, retrieving, and exporting selection tables
• Using annotations

Building spectrograms Building spectrograms

• Fourier transform
• Building a spectrogram
• Characteristics and limitations
• Spectrograms in R

Package seewave seewave

• Explore, modify and measure ‘wave’ objects
• Spectrograms and oscillograms
• Filtering and re-sampling
• Acoustic measurements

 

2.2 Homework

1. Use Raven Pro to annotate some of the signals found in the xeno-canto recordings you downloaded previously

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3 Day 3 (Video)

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3.1 Additional resources

3.1.1 Readings

• Arasco, A. G., Manser, M., Watson, S. K., Kyabulima, S., Radford, A. N., Cant, M. A., & Garcia, M. (2024). Testing the acoustic adaptation hypothesis with vocalizations from three mongoose species. Animal Behaviour, 187, 71-95.

 

Annotation software annotations

• Raven / audacity
• Open and explore recordings
• Modify-optimize visualization parameters
• Annotate signals

Quantifying acoustic signal structure Quantify structure

• Spectro-temporal measurements (spectro_analysis())
• Parameter description
• Harmonic content
• Cepstral coefficients (mfcc_stats())
• Cross-correlation (cross_correlation())
• Dynamic time warping (freq_DTW())
• Signal-to-noise ratio (sig2noise())
• Inflections (inflections())
• Parameters at other levels (song_analysis())

 

3.2 Homework

1. Double-check annotations using warbleR’s dedicated functions

• Create single spectrograms of each annotation
• Create full spectrograms of all sound files along with annotations
• Create catalogs

 

2. Double-check annotations using Raven (export data from R to Raven)

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4 Day 4 (Video)

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4.1 Additional resources

4.1.1 Readings

• Odom, K. J., Cain, K. E., Hall, M. L., Langmore, N. E., Mulder, R. A., Kleindorfer, S., … & Webster, M. S. (2021). Sex role similarity and sexual selection predict male and female song elaboration and dimorphism in fairy‐wrens. Ecology and evolution, 11(24), 17901-17919.

 

Quality control in recordings and annotations Quality checks

• Check and modify sound file format (check_wavs(), info_wavs(), duration_wavs(), mp32wav() y fix_wavs())
• Tuning spectrogram parameters (tweak_spectro())
• Double-checking selection tables (check_sels(), spectrograms(), full_spectrograms() & catalog())
• Re-adjusting selections (tailor_sels())

Characterizing hierarchical levels in acoustic signals

• Creating ‘song’ spectrograms (full_spectrograms(), spectrograms())
• ‘Song’ parameters (song_analysis())

 

4.2 Homework

1. Select best quality signals for analysis
2. Measure acoustic parameters
3. Summarize variation at higher hierachical levels (if necessary)

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5 Day 5 (Video)

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5.1 Additional resources

5.1.1 Readings

• Blog post: Potential issues of the ‘spectral parameters/PCA’ approach

• Blog post: Choosing the right method for measuring acoustic signal structure

 

Choosing the right method for quantifying structure Comparing methods

• Compare different methods for quantifying structure (compare_methods())

Quantifying acoustic spaces

• Intro to PhenotypeSpace
• Quanitfying space size
• Comparing sub-spaces