Blind source computer device identification from recorded calls

Abstract

This study investigates the use of blind source computer device identification for forensic investigation of the recorded VoIP call. It was found that a combination of mel-frequency cepstrum coefficients (MFCCs) and entropy as an intrinsic audio feature captures the specific frequency response due to the tolerance in the nominal values of the electronic components associated to individual computer device. By applying the supervised learning techniques such as naïve Bayesian, linear logistic regression, neural networks (NN), support vector machines (SVM) and sequential minimal optimization (SMO) classifier to the Entropy-MFCC features, state-of-the-art identification accuracy of above 99.8 % has been achieved on a set of 5 iMacs and 5 desktop PCs from the same model.