ohun is intended to facilitate the automated detection of sound events, providing functions to diagnose and optimize detection routines. Detections from other software can also be explored and optimized.


The main features of the package are:

  • The use of reference annotations for detection optimization and diagnostic
  • The use of signal detection theory indices to evaluate detection performance


The package offers functions for:

  • Curate references and acoustic data sets
  • Diagnose detection performance
  • Optimize detection routines based on reference annotations
  • Energy-based detection
  • Template-based detection

All functions allow the parallelization of tasks, which distributes the tasks among several processors to improve computational efficiency. The package works on sound files in ‘.wav’, ‘.mp3’, ‘.flac’ and ‘.wac’ format.

To install the latest developmental version from github you will need the R package remotes:

# install pacakge

#load package


Automatic sound event detection

Finding the position of sound events in a sound file is a challenging task. ohun offers two methods for automated sound event detection: template-based and energy-based detection. These methods are better suited for highly stereotyped or good signal-to-noise ratio (SNR) sounds, respectively. If the target sound events don’t fit these requirements, more elaborated methods (i.e. machine learning approaches) are warranted: