文章基本信息

标题：Identification of Fake Stereo Audio Using SVM and CNN
本地全文：下载
作者：Tianyun Liu ; Diqun Yan ; Rangding Wang 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2021
卷号：12
期号：7
页码：263
DOI：10.3390/info12070263
出版社：MDPI Publishing
摘要：The number of channels is one of the important criteria in regard to digital audio quality. Generally, stereo audio with two channels can provide better perceptual quality than mono audio. To seek illegal commercial benefit, one might convert a mono audio system to stereo with fake quality. Identifying stereo-faking audio is a lesser-investigated audio forensic issue. In this paper, a stereo faking corpus is first presented, which is created using the Haas effect technique. Two identification algorithms for fake stereo audio are proposed. One is based on Mel-frequency cepstral coefficient features and support vector machines. The other is based on a specially designed five-layer convolutional neural network. The experimental results on two datasets with five different cut-off frequencies show that the proposed algorithm can effectively detect stereo-faking audio and has good robustness.
关键词：stereo faking audio; audio forensics; MFCC; SVM; CNN stereo faking audio ; audio forensics ; MFCC ; SVM ; CNN