TY - GEN
T1 - Audio post-processing detection and identification based on audio features
AU - Zhan, Yunzhen
AU - Yuan, Xiaochen
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/19
Y1 - 2017/10/19
N2 - As an important communication medium, audios are easily modified or tampered during transmission; thus the authenticity of audios is of high importance. This paper mainly introduces a method to detect audio post-processing based on audio features; the Support Vector Machine (SVM) is applied for classification during the detection. In the proposed method, the Mel Frequency Cepstral Coefficient (MFCC) and the Linear Prediction Coding (LPC) of host audios are calculated as audio features, to which SVM is applied to judge the authenticity of the audios. Experimental results show that the proposed audio feature based method can not only verify the authenticity of speech audio, but also have a significant effect on detecting different types of post-processing operations.
AB - As an important communication medium, audios are easily modified or tampered during transmission; thus the authenticity of audios is of high importance. This paper mainly introduces a method to detect audio post-processing based on audio features; the Support Vector Machine (SVM) is applied for classification during the detection. In the proposed method, the Mel Frequency Cepstral Coefficient (MFCC) and the Linear Prediction Coding (LPC) of host audios are calculated as audio features, to which SVM is applied to judge the authenticity of the audios. Experimental results show that the proposed audio feature based method can not only verify the authenticity of speech audio, but also have a significant effect on detecting different types of post-processing operations.
KW - Audio Feature
KW - Audio Post-processing Detection
KW - Linear Prediction Coding (LPC)
KW - Mel Frequency Cepstral Coefficient (MFCC)
KW - Support Vector Machine (SVM)
UR - https://www.scopus.com/pages/publications/85036467636
U2 - 10.1109/ICWAPR.2017.8076681
DO - 10.1109/ICWAPR.2017.8076681
M3 - Conference contribution
AN - SCOPUS:85036467636
T3 - International Conference on Wavelet Analysis and Pattern Recognition
SP - 154
EP - 158
BT - Proceedings of 2017 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2017
PB - IEEE Computer Society
T2 - 2017 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2017
Y2 - 9 July 2017 through 12 July 2017
ER -