Vocal feature extraction and classification algorithms for Parkinson's Disease: oversampling method for imbalanced dataset

Sara Chiavassa

Vocal feature extraction and classification algorithms for Parkinson's Disease: oversampling method for imbalanced dataset.

Rel. Alessio Carullo, Alberto Vallan, Alessio Atzori. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2020

Abstract

This thesis work is part of the research field that investigates speech impairments in Parkinson’s Disease (PD) patients, in collaboration with DET and DENERG of Politecnico di Torino. The main goals are the early diagnosis and the evaluation of treatments effectiveness of PD through the processing and classification of voice signals. The available voice dataset was acquired in previous years at Centro Parkinson of Molinette hospital (TO) using a microphone in air and a throat contact microphone. The monitored patients belong to 3 different classes: healthy (HE), PD and pathological (PA) subjects and the tasks they performed are sustained vowel /a/, reading and free speech.

The first part of the thesis is focused on the signals pre-processing to subdivide voice signals into silence, voiced and unvoiced frames, according to three basic features: rms (root-mean-square value), fundamental frequency F0 and HNR (Harmonic-to-Noise Ratio)