Source microphone identification from speech recordings based on a Gaussian mixture model

Abstract

Microphone identification is a specific type of media forensics that investigates whether it is possible to identify the source microphone from speech recordings. The main aim of this study is to find out which of the several feature extraction techniques are best suited to the source microphone identification systems. We perform microphone identification experiments with 16 different microphones using 3 datasets. In order to improve the results on the datasets, we also investigate the important parameters that may affect the microphone identification performance. Our experimental results show that the proposed method is comparable to the existing studies in a closed-set identification rate.