MAAD's Work Flux are the steps (scripts) to follow to perform automatic classification of sounds of interest. These steps are based on the MAAD method.
Use the package manager pip to install MAAD library.
pip install upgrade -i https://test.pypi.org/simple/scikit-maadfrom maad.rois import find_rois_cwt
from maad import sound
s, fs = sound.load('./templates/BETA-_20161006_002000_section.wav') # loads a signal of interest as a floating point time series s.
# get the sample rate of the signal fs.
rois = find_rois_cwt(s, fs,
flims = (1000,4000), # frequency limits of the regions of interest.
tlen = 0.3, # time length of the regions of interest.
th = 0.0001, # threshold to filter the signal
display=True, figsize=(13,6))
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1_detection_and_feature_extraction.ipynb Detection of regions of interest and extraction of characteristics from regions of interest.
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2_data_processing.ipynb The regions of interest are labeled and the training and test sets are created.
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3_models_training.ipynb Supervised learning models are trained. It's found the hyperparameters of the models: logistic regression, support vector machine, random forest, multilayer perceptron.
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4_data_analysis.ipynb Visualize and analyze data.
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5_data_augmentation.ipynb Generate the files with background sound.
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6_data_augmentation_features_extraction.ipynb Extract the characteristics of the augmented data.
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7_data_augmentation_data_processing.ipynb The regions of interest of augmented data are labeled and the training and test sets are created.
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8_data_augmentation_models_training.ipynb Supervised learning models are trained.
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9_data_analysis_data_augmentation.ipynb Visualize and analyze data.
Developed by Juan Felipe Latorre Gil, you can contact me by email: jflatorreg@unal.edu.co or git.
This work was based on the preliminary work of the MAAD method carried out by Juan Sebastián Ulloa Chacón (julloa@humboldt.org.co). Thanks for the help.