Day 1
Readings
Reproducibility
- 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.
Intro to basic bioacoustics concepts
- 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)
Introduction Introduction
- How animal acoustic signals look like?
- Analytical workflow in bioacoustics research
- Advantages of programming
- Course outline
Basic R concepts R basics
- Object types in R
- Subsetting
- Style matters
- R documentation
Project assignment
Use the function query_xc() to check the availability of recordings for some bird species you have in mind for your project (do not download at this step) (check this brief tutorial on how to do that)
Subset the data frame returned by the function to get the subspecies/populations and vocalization type you are interested in (using base R subsetting tools)
Download the associated recordings using query_xc() again
Day 2
What is sound? Sound
- Sound as a time series
- Sound as a digital object
- Acoustic data in R
- ‘wave’ object structure
- ‘wave’ object manipulations
- additional formats
Project assignment
- Annotate target signals using Raven Pro (a subset of the recordings if working on automatic detection)
Day 3
Building spectrograms Building spectrograms
- Fourier transform
- Building a spectrogram
- Characteristics and limitations
- Spectrograms in R
Project assignment
- 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
- Double-check annotations using Raven (export data from R to Raven)
Day 4
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.
Package seewave seewave
- Explore, modify and measure ‘wave’ objects
- Spectrograms and oscillograms
- Filtering and re-sampling
- Acoustic measurements
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())
- Acoustic space using Principal Component Analysis
Project assignment
- Select best quality signals for analysis
- Measure acoustic parameters
- Summarize variation at higher hierachical levels (if necessary)
Day 5
Quality control in recordings and annotation 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())