1. Adaboost

--> Initialize AdaBoost class, pass number of desired weak learners as the argument: Let us say we want 30 classifiers

adaboost = AdaBoost(T = 30)

--> To TRAIN the model pass training data and labels to the ababoost_train function.

Shape: X_tr -> (no. of data points, no of features) eg. (7000, 50) Y_tr -> (no. of data points, 1) eg. (7000, 1)

model = adaboost.adaboost_train(X_tr, Y_tr)

--> To get new prediciton. Run this to perform prediction on the model after training. This function can be used for: X_train, X_val or X_test Input shape: (no. of data points, no of features) eg. (7000, 50)

predictions = adaboost.adaboost_predict(X_tr)

--> To save trained model:

adaboost.save_model()

--> To load a saved model:

adaboost.load_model(model_path):

2. Independent Component Analysis (ICA) Can be used to seperate mixed signals (audio) into seperate signals. Load the signls and stack them row-wise. Eg. load 4 wav files using librosa, stack. The matrix will be of size (4, sampling_rate * audio_len(time)). Then pass the matrix through the function. The output will also have 4 rows. Each row is a seperated signal (audio). Can be saved as audio file using librosa.