Identification of fake stereo audio

TL;DR

This paper presents a new method for detecting fake stereo audio by analyzing MFCC features and using SVM classifiers, demonstrating effectiveness across multiple datasets.

Contribution

It introduces a stereo faking corpus and proposes an SVM-based detection algorithm utilizing MFCC features, addressing a less-investigated audio forensic issue.

Findings

Effective detection of stereo faking audio achieved

High robustness demonstrated across datasets

MFCC features are effective for distinguishing real and fake stereo

Abstract

Channel is one of the important criterions for digital audio quality. General-ly, stereo audio two channels can provide better perceptual quality than mono audio. To seek illegal commercial benefit, one might convert mono audio to stereo one with fake quality. Identifying of stereo faking audio is still a less-investigated audio forensic issue. In this paper, a stereo faking corpus is first present, which is created by Haas Effect technique. Then the effect of stereo faking on Mel Frequency Cepstral Coefficients (MFCC) is analyzed to find the difference between the real and faked stereo audio. Fi-nally, an effective algorithm for identifying stereo faking audio is proposed, in which 80-dimensional MFCC features and Support Vector Machine (SVM) classifier are adopted. The experimental results on three datasets with five different cut-off frequencies show that the proposed algorithm can ef-fectively detect stereo faking audio and achieve a good robustness.