Article title: Estimating ecoacoustic activity in the Amazon rainforest through Information Theory quantifiers. PDF
Previous title: 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.
A copy of the preprocessed dataset to reproduce the main results of the article is now delivered with this repository. The dataset files are located in the pkl_dataset folder.
Anyway, a copy of this data can be downloaded through the link here. If you choose to download the dataset on this link, note that the downloaded file must be unzipped 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
pip install EGCI
import numpy as np
import soundfile as sf
import EGCI
import matplotlib.pyplot as plt
# download a record file from this url: "https://drive.google.com/file/d/1QL5GimLjGLKBIiMzoa7VXlCR4GCpWBwc/view?usp=drivesdk"
# load this record
x, fs = sf.read('Adenomera andre.wav') # record of an anuran call
lag = 256 # time lag
C, H, J = EGCI.index(x, lag=lag) # C is the EGCI
boundaries_C, boundaries_H = EGCI.boundaries(lag) # these boundaries are only useful for plotting
plt.figure()
plt.plot(boundaries_H, boundaries_C, '--k')
plt.scatter(H, C, marker='.', s=100, label='Adenomera andre')
plt.xlabel('Entropy')
plt.ylabel('EGCI (Complexity)')
plt.xlim([0, 1])
plt.ylim([0, np.max(boundaries_C)+0.01])
plt.title('Adenomera andre.wav')
plt.legend(loc = 'best')
plt.show()