scikit-maad, soundscape analysis in Python
scikit-maad is an open source Python package dedicated to the quantitative analysis of environmental audio recordings. This package was designed to (1) load and process digital audio, (2) segment and find regions of interest, (3) compute acoustic features, and (4) estimate sound pressure level. This workflow opens the possibility to scan large audio datasets and use powerful machine learning techniques, allowing to measure acoustic properties and identify key patterns in all kinds of soundscapes.
- Python >= 3.5
- NumPy >= 1.13
- SciPy >= 0.18
- scikit-image >= 0.14
scikit-maad is hosted on PyPI. To install, run the following command in your Python environment:
$ pip install scikit-maad
To install the latest version from source clone the master repository and from the top-level folder call:
$ python setup.py install
Examples and documentation
- See https://scikit-maad.github.io for a complete reference manual and example gallery.
- In depth information related to the Multiresolution Analysis of Acoustic Diversity implemented in scikit-maad was published in: Ulloa, J. S., Aubin, T., Llusia, D., Bouveyron, C., & Sueur, J. (2018). Estimating animal acoustic diversity in tropical environments using unsupervised multiresolution analysis. Ecological Indicators, 90, 346–355
Contributions and bug report
Improvements and new features are greatly appreciated. If you would like to contribute developing new features or making improvements to the available package, please refer to our wiki. Bug reports and especially tested patches may be submitted directly to the bug tracker.
About the authors
This work started in 2016 at the Museum National d'Histoire Naturelle (MNHN) in Paris, France. It was initiated by Juan Sebastian Ulloa, supervised by Jérôme Sueur and Thierry Aubin at the Muséum National d'Histoire Naturelle and the Université Paris Saclay respectively. Python functions have been added by Sylvain Haupert, Juan Felipe Latorre and Juan Sebastián Ulloa in 2018. New features are currently being developed and a stable release will be available by 2021.
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