Unsupervised classification#

Unsupervised classifications is an alternative to tedious manual classification of song types: Use DAS via the GUI or the command line to detect anything that you think is song and then classify song into different types afterwards. The song types discovered with unsupervised methods can then be used to create a training dataset for training DAS to directly label the different song types.

DAS-unsupervised provides tools for applying this approach with a focus on pre-processing acoustic signals for unsupervised classification:

  • extract waveforms or spectrograms of acoustic events from a recording

  • normalize the duration, center frequency, amplitude, or sign of waveform/spectrograms

Unsupervised classification itself is performed using existing libraries:

Code for the unsupervised classification can be found at https://github.com/janclemenslab/DAS_unsupervised.


We illustrate different pre-processing and classification strategies using three different examples



Code from the following open source packages was modified and integrated into das-unsupervised:

Data sources:


  1. T Sainburg, M Thielk, TQ Gentner (2020) Latent space visualization, characterization, and generation of diverse vocal communication signals. Biorxiv . https://doi.org/10.1101/870311

  2. J Clemens, P Coen, F Roemschied, T Perreira, D Mazumder, D Aldorando, D Pacheco, M Murthy (2018) Discovery of a New Song Mode in Drosophila Reveals Hidden Structure in the Sensory and Neural Drivers of Behavior. Current Biology 28, 2400–2412.e6 (2018). https://doi.org/10.1016/j.cub.2018.06.011

  3. D Stern (2014). Reported Drosophila courtship song rhythms are artifacts of data analysis. BMC Biology

  4. A Ivanenko, P Watkins, MAJ van Gerven, K Hammerschmidt, B Englitz (2020) Classifying sex and strain from mouse ultrasonic vocalizations using deep learning. PLoS Comput Biol 16(6): e1007918. https://doi.org/10.1371/journal.pcbi.1007918

  5. D Nicholson, JE Queen, S Sober (2017). Bengalese finch song repository. https://doi.org/10.6084/m9.figshare.4805749.v5