Double Compressed AMR Audio Detection Using Long-Term Features and Deep Neural Networks

Abstract

Detecting double compressed audio files is an important problem for audio forensics applications such as audio forgery detection and determining the authenticity of an audio file appearing as an evidence. In this paper, we focus on detecting double compressed adaptive multi-rate (AMR) audio using deep neural network (DNN) classifier and long-term average spectrum (LTAS) and long-term average cepstrum (LTAC) features. Experiments conducted on TIMIT database show that compression rate has a significant impact on the performance. LTAS and LTAC features yield similar performance with slight differences. Removing unvoiced audio frames is found to reduce the detection accuracy and multi-condition training does not bring any performance improvement.