ECI (Ecoacoustic Complexity Index)

Amazon rainforest soundscape characterized by Information Theory quantifiers. PDF

The jupyter notebooks available here allows to reproduce the images and results included in the PDF article.

In order to run the programs you need to download an additional dataset file here.

The downloaded file should be pkl_datasets.zip, simply unzip the files into a folder called pkl_datasets.

python : 3.6.9
pandas : 0.25.3
numpy : 1.17.3
matplotlib : 3.1.2
scipy : 1.3.1
statsmodels : 0.10.2
soundfile : 0.10.3
colorednoise : 1.1.1
tqdm

Name		Name	Last commit message	Last commit date
Latest commit History 43 Commits
Rain		Rain
boundary_files		boundary_files
coloredNoises		coloredNoises
mamiraua_samples		mamiraua_samples
wav_files		wav_files
.gitignore		.gitignore
Color noise generator.ipynb		Color noise generator.ipynb
ECI_Peruvian_Amazon_Data.ipynb		ECI_Peruvian_Amazon_Data.ipynb
EcoacousticComplexityIndexDeepLearningPipeline.pdf		EcoacousticComplexityIndexDeepLearningPipeline.pdf
Example_of_EGCI_calculation.ipynb		Example_of_EGCI_calculation.ipynb
Fig1.ipynb		Fig1.ipynb
Fig10.ipynb		Fig10.ipynb
Fig11.ipynb		Fig11.ipynb
Fig12.ipynb		Fig12.ipynb
Fig13.ipynb		Fig13.ipynb
Fig2.ipynb		Fig2.ipynb
Fig3.ipynb		Fig3.ipynb
Fig4.ipynb		Fig4.ipynb
Fig5.ipynb		Fig5.ipynb
Fig6.ipynb		Fig6.ipynb
Fig7.ipynb		Fig7.ipynb
Fig8.ipynb		Fig8.ipynb
Fig9.ipynb		Fig9.ipynb
LICENSE		LICENSE
README.md		README.md
fluviometry_data.csv		fluviometry_data.csv
infromation_theory_utils.py		infromation_theory_utils.py

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