Double Compressed AMR Audio Detection Using Spectral Features With Temporal Segmentation

Abstract

Double compressed (DC) AMR audio detection is an important audio forensic problem which is used to authenticate the originality of an auido recording. Majority of the existing studies use audio features extracted from the AMR encoder parameters such as linear prediction (LP) coefficients. Recently, we proposed to use the long-term average spectrum (LTAS) features for DC AMR audio detection and promising results were achieved. In this paper, we propose a novel feature extraction techniques which does not require any prior knowledge about the details of the encoding and decoding processes of the AMR codec. The proposed features are extracted from the temporal segmentation of the short-term Fourier transform (STFT) representation of the audio signal. The proposed features are then classified using deep neural network (DNN) classifier. Experimental results conducted on two different databases show that the proposed features considerably outperform the long-term average spectrum (LTAS) features. The average detection rate is improved from 92.44% to 96.48% on MDSVC dataset and from 80.95% to 83.67% on TIMIT database with the proposed features.