utils

Various scripts for machine learning

extract_mfcc_libri_pools.py

function for Libri speech audio pcm data base that creates dict: keys - speaker label, value - path to flac file then creates callable class; during calling instance estimate MFCC using librosa lib and does some other minor preprocessing steps like: convert to mono, resampling to 16 kHz, trmming then using multiprocessing lib start process-based parallelism

pca_definition.py

function creates PCA algorithm using theoretical description

ensamble machine learning algorithms (ensambleML)

Jupyter notobook shows how to use ensamble methods like Bagging (BaggingClassifier), Boosting (GradientBoostingClassifier, AdaBoostClassifier, XGBoost), Stacking (used all above and Logistic Regression or SVM as a meta models) and Blending (used all above and Logistic Regression as a meta model). In example data from Kaggle were used.