Soundclim analysis workflow

This repository compiles scripts and modules to search for soundmarks in audio recordings using machine learning.

Prerequisites

• Python version 3.6 or higher and the following packages: numpy, pandas, scipy, scikit-image, scikit-learn, scikit-maad. To install, run in your terminal console:

pip install numpy pandas scipy scikit-image scikit-learn scikit-maad

How to run

Data preparation

Check consistency of the full dataset: sample rate, number of channels, bits per sample

soxi -r *.wav | uniq  # sample rate
soxi -b *.wav | uniq  # number of bits per sample

If not homogeneous, they should be resampled:

cd <DIR>
mkdir samp
for i in *.wav; do sox $i `echo ./samp/$i` rate 22050 ;done

Workflow

Note: utility functions are stored in the module soundclim_utilities.py and classif_fcns.py

Compile train dataset 1_compile_dataset.py

• Segment and characterise the regions of interest
• Select ROIs to manually annotate and export comma separated file

Format training dataset for manual annotation

• Format the training dataset with associted metadata as an RData file 2_format_trainds.R
• Mix a single audio file with all ROIs and export as wav file with text annotation file 3_mix_audio_trainds.R
• Manually annotate ROIs using Audacity manual_annotation_protocol.html
• Export annotation and match with features 4_export_features_and_mannot_to_csv.R

Tune statistical classifier and deploy

• Train and tune multiple statistical classifiers 5_tune_clf_simple.py
• Predict and validate using test dataset 6_batch_predict_rois.py

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Authors and contact

Juan Sebastián Ulloa