Sound Classification by the TIAGo Service Robot for Healthcare Applications

Abstract

The goal of this research is to compare several classification algorithms to determine the effect of the features number for Linear Predictive Coding and Linear Predictive Cepstral Coefficients upon the averaged correct classification rate, in the context of audio signals, part of them used in healthcare applications, recorded by a service robot. The standard deviation and the required computation time, in the case of every classifier, are also illustrated. The best correct classification rate was obtained in the case of Linear Predictive Cepstral Coefficients using Support Vector Machines, for 10-fold cross-validation.