A curated list of open technology projects to sustain a stable climate, energy supply, biodiversity and natural resources.

scikit-maad

Enables quantitative analysis of environmental audio, offering tools for processing, segmenting, and computing acoustic features.
https://github.com/scikit-maad/scikit-maad

Category: Biosphere
Sub Category: Bioacoustics and Acoustic Data Analysis

Keywords

acoustic-indices bioacoustics ecoacoustics pattern-recognition signal-processing sound-pressure-level

Keywords from Contributors

archiving measur transforms optimize observation generic animals compose projection conversion

Last synced: about 22 hours ago
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Repository metadata

Open-source and modular toolbox for quantitative soundscape analysis in Python

README.md

scikit-maad

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.

PyPI version
Project Status: Active – The project has reached a stable, usable state and is being actively developed.
License
DOI
Maintenance
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Operating Systems

scikit-maad seamlessly supports Linux, macOS, and Windows operating systems.

Interpreter

The latest version of scikit-maad requires one of these interpreters:

  • Python 3.9, 3.10, 3.11 or 3.12

Packages dependency

scikit-maad requires these Python packages to run:

  • matplotlib >=3.6
  • numpy >= 1.21
  • pandas >= 1.5
  • scikit-image >= 0.19
  • scipy >= 1.8

Installing from PyPI

scikit-maad is hosted on PyPI. The easiest way to install the package is using pip the standard package installer for Python:

$ pip install scikit-maad

Quick start

The package is imported as maad. To use scikit-maad tools, audio must be loaded as a numpy array. The function maad.sound.load is a simple and effective way to load audio from disk. For example, download the spinetail audio example to your working directory. You can load it and then apply any analysis to find regions of interest or characterize your audio signals:

from maad import sound, rois
s, fs = sound.load('spinetail.wav')
rois.find_rois_cwt(s, fs, flims=(4500,8000), tlen=2, th=0, display=True)

For advance users

Installing from source

If you are interested in developing new features for scikit-maad or working with the latest version, clone and install it:

$ git clone https://github.com/scikit-maad/scikit-maad.git
$ cd scikit-maad
$ pip install --editable .

Running tests

Install the test requirements:

$ pip install pytest

And run the tests:

$ cd scikit-maad
$ pytest

Examples and documentation

Runnin all examples requires to install the following packages :

Citing this work

If you find scikit-maad usefull for your research, please consider citing it as:

  • Ulloa, J. S., Haupert, S., Latorre, J. F., Aubin, T., & Sueur, J. (2021). scikit‐maad: An open‐source and modular toolbox for quantitative soundscape analysis in Python. Methods in Ecology and Evolution, 2041-210X.13711. https://doi.org/10.1111/2041-210X.13711

or use our citing file for custom citation formats.

Feedback and contributions

Improvements and new features are greatly appreciated. If you would like to contribute submitting issues, developing new features or making improvements to scikit-maad, please refer to our contributors guide.
To create a positive social atmosphere for our community, we ask contributors to adopt and enforce our code of conduct.

About the project

In 2018, we began to translate a set of audio processing functions from Matlab to an open-source programming language, namely, Python. These functions provided the necessary tools to replicate the Multiresolution Analysis of Acoustic Diversity (MAAD), a method to estimate animal acoustic diversity using unsupervised learning (Ulloa et al., 2018). We soon realized that Python provided a suitable environment to extend these core functions and to develop a flexible toolbox for our research. During the past few years, we added over 50 acoustic indices, plus a module to estimate the sound pressure level of audio events. Furthermore, we updated, organized, and fully documented the code to make this development accessible to a much wider audience. This work 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 (Universidad Nacional de Colombia) and Juan Sebastián Ulloa (Instituto de Investigación de Recursos Biológicos Alexander von Humboldt). For an updated list of collaborators, check the contributors list.

License

To support reproducible research, the package is released under the BSD open-source licence, which allows unrestricted redistribution for commercial and private use.

Citation (CITATION.bib)

@article{ulloa_etal_scikitmaad_2021,
	title = {scikit‐maad: {An} open‐source and modular toolbox for quantitative soundscape analysis in {Python}},
	issn = {2041-210X, 2041-210X},
	shorttitle = {scikit‐maad},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/2041-210X.13711},
	doi = {10.1111/2041-210X.13711},
	language = {en},
	urldate = {2021-10-04},
	journal = {Methods in Ecology and Evolution},
	author = {Ulloa, Juan Sebastián and Haupert, Sylvain and Latorre, Juan Felipe and Aubin, Thierry and Sueur, Jérôme},
	month = sep,
	year = {2021},
	pages = {2041--210X.13711},
}

Owner metadata


GitHub Events

Total
Last Year

Committers metadata

Last synced: 5 days ago

Total Commits: 735
Total Committers: 11
Avg Commits per committer: 66.818
Development Distribution Score (DDS): 0.54

Commits in past year: 51
Committers in past year: 3
Avg Commits per committer in past year: 17.0
Development Distribution Score (DDS) in past year: 0.431

Name Email Commits
Sylvain Haupert s****t@m****r 338
Juan Sebastian Ulloa j****a@g****m 260
saguileran s****n@u****o 42
scikit-maad 4****d 41
jflatorreg j****g@u****o 40
Juan Sebastian Ulloa j****a@J****l 7
dependabot[bot] 4****] 3
Pierre Aumond p****d@i****r 1
GabrielPerilla g****a@h****o 1
Juan Sebastian Ulloa l****a@g****m 1
Juan Cañas j****s@J****l 1

Committer domains:


Issue and Pull Request metadata

Last synced: 2 days ago

Total issues: 46
Total pull requests: 63
Average time to close issues: 3 months
Average time to close pull requests: 18 days
Total issue authors: 26
Total pull request authors: 10
Average comments per issue: 1.65
Average comments per pull request: 0.25
Merged pull request: 47
Bot issues: 0
Bot pull requests: 7

Past year issues: 12
Past year pull requests: 8
Past year average time to close issues: 21 days
Past year average time to close pull requests: 2 days
Past year issue authors: 8
Past year pull request authors: 4
Past year average comments per issue: 1.42
Past year average comments per pull request: 0.63
Past year merged pull request: 6
Past year bot issues: 0
Past year bot pull requests: 0

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/scikit-maad/scikit-maad

Top Issue Authors

  • Bengt (9)
  • dariodematties (3)
  • jokot025 (3)
  • saguileran (3)
  • shaupert (3)
  • juancanyas (2)
  • charlie-garcia (2)
  • charleygros (2)
  • kroegern1 (2)
  • gg4u (1)
  • lizferguson5 (1)
  • ss3443 (1)
  • apotenza (1)
  • YizharLavner (1)
  • sammlapp (1)

Top Pull Request Authors

  • shaupert (23)
  • juansulloa (18)
  • dependabot[bot] (7)
  • scikit-maad (5)
  • Bengt (4)
  • ghost (2)
  • Ryanff72 (1)
  • arpit-omprakash (1)
  • jscanass (1)
  • pierromond (1)

Top Issue Labels

  • bug (18)
  • enhancement (11)
  • warning (3)
  • dependencies (1)
  • question (1)

Top Pull Request Labels

  • dependencies (7)
  • enhancement (2)
  • bug (1)

Package metadata

pypi.org: scikit-maad

Open-source and modular toolbox for quantitative soundscape analysis in Python

  • Homepage: https://github.com/scikit-maad/scikit-maad
  • Documentation: https://scikit-maad.github.io/
  • Licenses: BSD License
  • Latest release: 1.5.1 (published 16 days ago)
  • Last Synced: 2025-04-25T12:09:25.865Z (2 days ago)
  • Versions: 13
  • Dependent Packages: 7
  • Dependent Repositories: 68
  • Downloads: 9,880 Last month
  • Docker Downloads: 107
  • Rankings:
    • Dependent packages count: 1.256%
    • Downloads: 1.781%
    • Dependent repos count: 1.808%
    • Docker downloads count: 3.236%
    • Average: 4.226%
    • Stargazers count: 7.904%
    • Forks count: 9.37%
  • Maintainers (2)

Dependencies

.github/workflows/ci-cd.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
.github/workflows/python-compatibility.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
pyproject.toml pypi
  • matplotlib >=3.6
  • numpy >=1.21
  • pandas >=1.5
  • resampy >=0.4
  • scikit-image >=0.19
  • scipy >=1.8
requirements.txt pypi
  • matplotlib >=3.6
  • numpy >=1.21
  • pandas >=1.5
  • resampy >=0.4
  • scikit-image >=0.19
  • scipy >=1.8.0

Score: 16.42699831550709