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https://github.com/janclemenslab/das_unsupervised
Deep Audio Segmenter, unsupervised
https://github.com/janclemenslab/das_unsupervised
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Deep Audio Segmenter, unsupervised
- Host: GitHub
- URL: https://github.com/janclemenslab/das_unsupervised
- Owner: janclemenslab
- License: mit
- Created: 2020-10-30T14:36:57.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2022-01-29T13:44:37.000Z (almost 3 years ago)
- Last Synced: 2024-11-04T16:06:53.334Z (2 months ago)
- Language: Python
- Homepage:
- Size: 14.1 MB
- Stars: 8
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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](https://umap-learn.readthedocs.io/)
- clustering: [hdbscan](https://hdbscan.readthedocs.io/) or [scikit-learn](https://scikit-learn.org/stable/modules/clustering.html)
Can be used in combination with [DAS](https://github.com/janclemenslab/das), a deep learning based method for the supervised annotation of acoustic signals.
## Install via conda
```shell
conda create -y -n umap -c conda-forge python=3.9
conda activate umap
conda install -y -c ncb -c conda-forge numpy scipy scikit-learn matplotlib colorcet seaborn librosa pillow umap-learn hdbscan jupyterlab ipykernel
pip install noisereduce
pip install das_unsupervised --no-deps
```
## Demos
Illustration of the workflow and the method using vocalizations from:
- [flies](demo/flies.ipynb)
- [mice](demo/mice.ipynb)
- [birds](demo/birds.ipynb)
## Acknowledgements
Code from the following open source packages was modified and integrated into das-unsupervised:
- [avgn](https://github.com/timsainb/avgn_paper) (Sainburg et al. 2020)
- [noisereduce](https://pypi.org/project/noisereduce)
- [fly pulse classifier](https://github.com/murthylab/MurthyLab_FlySongSegmenter) (Clemens et al. 2018)
Data sources:
- flies: [David Stern](https://www.janelia.org/lab/stern-lab/tools-reagents-data) (Stern, 2014)
- mice: data provided by Kurt Hammerschmidt (Ivanenko et al. 2020)
- birds: [Bengalese finch song repository](https://doi.org/10.6084/m9.figshare.4805749.v5) (Nicholson et al. 2017)
## References
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]()