Session 1 – 04:00:00 – Introduction
What animal acoustic signals look like?
Analytical workflow in bioacoustics research
Advantages of programming
Session 2 – 04:00:00 – What is sound?
Sound as a time series
Sound as a digital object
Acoustic data in R
‘wave’ object structure
‘wave’ object manipulations
Additional formats
Overview of other audio file formats (e.g., MP3, FLAC) and how they can be converted and used in R for bioacoustic analysis.
Session 3- 02:00:00 – Building spectrograms
Fourier transform
Building a spectrogram (characteristics and limitations)
Step-by-step guide on how to construct spectrograms, including parameter selection (e.g.,
window size, overlap) and interpretation of the resulting visual representations.
Session 4- 02:00:00 – Spectrograms in R
Practical session on generating and customizing spectrograms in R using the seewave package. Participants will create spectrograms from their own data. Using the package seewave we will explore, modify and measure ‘wave’ objects This includes exercises on filtering, re-sampling, and extracting acoustic features and will include spectrograms & oscillograms, filtering & re-sampling and acoustic measurements. We will use the seewave package to perform detailed acoustic measurements, such as peak frequency, dominant frequency, and frequency range. Practical examples will be provided.
Session 5- 02:00:00 – Annotations
This session introduces the Raven Pro interface and its key features. Participants will learn how to navigate the software, make selections within sound files, and perform basic measurements such as duration and frequency. The session also covers saving, retrieving, and exporting selection tables, along with best practices for data organization. Finally, participants will explore annotation techniques, including the use of labels and notes to mark significant events in acoustic recordings.
Session 6 – 02:00:00 – Quantifying Acoustic Signal Structure
This session focuses on methods for extracting and analyzing the structural characteristics of acoustic signals using R. Participants will be introduced to the spectro-analysis() function for generating spectro-temporal measurements and describing signals in terms of duration, frequency, and amplitude. The session covers analysis of harmonic content and introduces Mel-frequency cepstral coefficients (MFCCs) using mfcc_stats() to assess timbral features.
Additional tools such as cross_correlation() and freq_DTW() will be used to compare signals through cross-correlation and dynamic time warping techniques. Participants will also learn to calculate signal-to-noise ratios with sig2noise() and identify pitch inflections using inflections(). The session concludes with song_analysis() to examine acoustic patterns at higher hierarchical levels, such as entire songs or vocal sequences.
Session 7- 04:00:00 – Annotations
This session covers essential quality control techniques for managing annotated acoustic data. Participants will learn how to compile catalogs of annotated sound files for reference or analysis, and use functions such as check_wavs(), info_wavs(), and fix_wavs() to verify and correct sound file formats and integrity. Tools like mp32wav() enable audio format conversion as part of preprocessing workflows.
The session also includes tuning spectrogram parameters using tweak_spectro() to improve signal visualization, and verifying annotation accuracy through tools like check_sels() and catalog(). Participants will practice refining annotations with tailor_sels() and learn how to build and interpret higher-level spectrograms representing full vocal sequences using full_spectrograms() and spectrograms(). Finally, the song_analysis() function will be used to quantify song-level parameters such as duration, element count, and rate.
Session 8 – 04:00:00 – Choosing the Right Method for Quantifying Structure
This session focuses on evaluating and selecting appropriate methods for quantifying the structure of acoustic signals. Participants will use the compare methods() function to assess different analytical approaches and understand their respective strengths and limitations. The session introduces the concept of acoustic spaces through the PhenotypeSpace framework, enabling the visualization and comparison of vocal diversity across datasets.
Participants will explore techniques for measuring the size of acoustic spaces to assess variability and complexity, and learn how to compare subspaces to analyse differences between species, populations, or other groups. Each topic is supported with clear explanations, practical examples, and hands-on exercises to build proficiency in applying these tools within the R environment for bioacoustics research.
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