sound-classification-validation

Feature selection, training and validation for the ADORE sound classifier

Prerequisites

To run the code you need:

• libXtract Python bindings, source available https://github.com/jamiebullock/LibXtract. Make sure to install Swig beforehand or Python bindings will not be built.
• sox for sound preprocessing

Workflow

• Download the dataset
• Process the dataset using prepare_audio.sh
• Calculate features in a given dataset using feature_calculation.py. Choose saving location inside the script.
• Train the classifier with feature selection using feature_selection.py.
• Once you have the classifier and feature list, you can do different validation steps

Validation

• Perform cross validation on the dataset using generate_conf_matrices_train_test.py.
• Record new dataset using a robot with play_record.py, then calculate features on that dataset (using feature_calculation.py) and validate the existing classifier using classifier_recordings.py.