Unsupervised classification

Unsupervised classifications is an alternative to tedious manual classification of song types: Use DeepSS 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 DeepSS to directly label the different song types.

DeepSS-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:

Examples

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

Acknowledgements

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

Data sources:

References

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

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