Audio post-processing detection and identification based on audio features

Yunzhen Zhan; Xiaochen Yuan

doi:10.1109/ICWAPR.2017.8076681

Audio post-processing detection and identification based on audio features

Yunzhen Zhan, Xiaochen Yuan

Macau University of Science and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings of 2017 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2017
Publisher	IEEE Computer Society
Pages	154-158
Number of pages	5
ISBN (Electronic)	9781538604106
DOIs	https://doi.org/10.1109/ICWAPR.2017.8076681
Publication status	Published - 19 Oct 2017
Externally published	Yes
Event	2017 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2017 - Ningbo, China Duration: 9 Jul 2017 → 12 Jul 2017

Publication series

Name	International Conference on Wavelet Analysis and Pattern Recognition
Volume	1
ISSN (Print)	2158-5695
ISSN (Electronic)	2158-5709

Conference

Conference	2017 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2017
Country/Territory	China
City	Ningbo
Period	9/07/17 → 12/07/17

Keywords

Audio Feature
Audio Post-processing Detection
Linear Prediction Coding (LPC)
Mel Frequency Cepstral Coefficient (MFCC)
Support Vector Machine (SVM)

Access to Document

10.1109/ICWAPR.2017.8076681

Cite this

Zhan, Y., & Yuan, X. (2017). Audio post-processing detection and identification based on audio features. In Proceedings of 2017 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2017 (pp. 154-158). Article 8076681 (International Conference on Wavelet Analysis and Pattern Recognition; Vol. 1). IEEE Computer Society. https://doi.org/10.1109/ICWAPR.2017.8076681

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title = "Audio post-processing detection and identification based on audio features",

abstract = "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.",

keywords = "Audio Feature, Audio Post-processing Detection, Linear Prediction Coding (LPC), Mel Frequency Cepstral Coefficient (MFCC), Support Vector Machine (SVM)",

author = "Yunzhen Zhan and Xiaochen Yuan",

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doi = "10.1109/ICWAPR.2017.8076681",

language = "English",

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Zhan, Y & Yuan, X 2017, Audio post-processing detection and identification based on audio features. in Proceedings of 2017 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2017., 8076681, International Conference on Wavelet Analysis and Pattern Recognition, vol. 1, IEEE Computer Society, pp. 154-158, 2017 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2017, Ningbo, China, 9/07/17. https://doi.org/10.1109/ICWAPR.2017.8076681

Audio post-processing detection and identification based on audio features. / Zhan, Yunzhen; Yuan, Xiaochen.
Proceedings of 2017 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2017. IEEE Computer Society, 2017. p. 154-158 8076681 (International Conference on Wavelet Analysis and Pattern Recognition; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Zhan, Yunzhen

AU - Yuan, Xiaochen

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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)

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Audio post-processing detection and identification based on audio features

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Keywords

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