Audio amplitude-level quantification vector for identification of audio postprocessing operation

Zekun Chen, Xiaochen Yuan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Audio tampering is typically followed by post-processing operations to mask the artifacts potentially perceptible by human ears and blur the traces of tampering. However, research on the issue of audio post-processing identification is still a blanket. This paper mainly introduces a method to identify audio post-processing operations. A new audio feature - Audio Amplitude-Level Quantification Vector (AQV) is proposed, then the probability distributions of AQV of audio are calculated and extracted as audio features which are then used for identification of various audio processing. During the detection, the K-Nearest Neighbors (KNN) classifier is applied for classification. Experimental results show that the proposed AQV method can not only verify the authenticity of the speech audio, but also have a significant effect on identifying different types of post-processing operations.

Original languageEnglish
Title of host publicationProceedings - 2018 International Conference on Sensor Networks and Signal Processing, SNSP 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages226-230
Number of pages5
ISBN (Electronic)9781538674130
DOIs
Publication statusPublished - 2 Jul 2018
Externally publishedYes
Event1st International Conference on Sensor Networks and Signal Processing, SNSP 2018 - Xi'an, China
Duration: 28 Oct 201831 Oct 2018

Publication series

NameProceedings - 2018 International Conference on Sensor Networks and Signal Processing, SNSP 2018

Conference

Conference1st International Conference on Sensor Networks and Signal Processing, SNSP 2018
Country/TerritoryChina
CityXi'an
Period28/10/1831/10/18

Keywords

  • Audio Amplitude-Level Quantification Vector (AQV)
  • Audio Feature
  • Audio Post-processing Detection
  • K-Nearest Neighbors (KNN)

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