"""! @brief Example 23 @details Audio event detection. Classification performance @author Theodoros Giannakopoulos {[email protected]} """ import numpy as np import utilities as ut from pyAudioAnalysis.MidTermFeatures import directory_feature_extraction as dW if __name__ == '__main__': # extract features, concatenate feature matrices and normalize: f1, _, fn1 = dW("../data/activity_sounds/cupboards", 1, 1, 0.05, 0.05) f2, _, fn1 = dW("../data/activity_sounds/door", 1, 1, 0.05, 0.05) f3, _, fn1 = dW("../data/activity_sounds/silence", 1, 1, 0.05, 0.05) f4, _, fn1 = dW("../data/activity_sounds/walk", 1, 1, 0.05, 0.05) x = np.concatenate((f1, f2, f3, f4), axis=0) y = np.concatenate((np.zeros(f1.shape[0]), np.ones(f2.shape[0]), 2 * np.ones(f3.shape[0]), 3 * np.ones(f4.shape[0]))) print(x.shape, y.shape) # train svm and get aggregated (average) confusion matrix, accuracy and f1 cm, acc, f1 = ut.svm_train_evaluate(x, y, 2, C=2) # visualize performance measures ut.plotly_classification_results(cm, ["cupboards", "door", "silence", "walk"]) print(acc, f1)
"""! @brief Example 24 @details Soundscape quality classification (through svm classifier) @author Theodoros Giannakopoulos {[email protected]} """ import numpy as np from pyAudioAnalysis.MidTermFeatures import directory_feature_extraction as dW import utilities as ut if __name__ == '__main__': # get features from folders (all classes): f1, _, fn1 = dW("../data/soundScape_small/1/", 2, 1, 0.1, 0.1) f3, _, fn1 = dW("../data/soundScape_small/3/", 2, 1, 0.1, 0.1) f5, _, fn1 = dW("../data/soundScape_small/5/", 2, 1, 0.1, 0.1) x = np.concatenate((f1, f3, f5), axis=0) y = np.concatenate((np.zeros(f1.shape[0]), 1 * np.ones(f3.shape[0]), 2 * np.ones(f5.shape[0]))) # train svm and get aggregated (average) confusion matrix, accuracy and f1 cm, acc, f1 = ut.svm_train_evaluate(x, y, 10, C=10, use_regressor=False) # visualize performance measures ut.plotly_classification_results(cm, ["q_1", "q_3", "q_5"]) print(acc, f1)