Tools for unsupervised classification of acoustic signals.
Project description
Tools for unsupervised classification of acoustic signals
DAS-unsupervised provides tools for 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:
- dimensionality reduction: umap
- clustering: hdbscan or scikit-learn
Can be used in combination with DAS, a deep learning based method for the supervised annotation of acoustic signals.
Installation
pip install das-unsupervised
Demos
Illustration of the workflow and the method using vocalizations from:
Acknowledgements
Code from the following open source packages was modified and integrated into das-unsupervised:
- avgn (Sainburg et al. 2020)
- noisereduce
- fly pulse classifier (Clemens et al. 2018)
Data sources:
- flies: David Stern (Stern, 2014)
- mice: data provided by Kurt Hammerschmidt (Ivanenko et al. 2020)
- birds: Bengalese finch song repository (Nicholson et al. 2017)
References
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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
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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
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D Stern (2014). Reported Drosophila courtship song rhythms are artifacts of data analysis. BMC Biology
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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
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D Nicholson, JE Queen, S Sober (2017). Bengalese finch song repository. https://doi.org/10.6084/m9.figshare.4805749.v5
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