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Robust Electric Network Frequency Estimation with Rank Reduction and Linear Prediction

Published:23 October 2018Publication History
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Abstract

This article deals with the problem of Electric Network Frequency (ENF) estimation where Signal to Noise Ratio (SNR) is an essential challenge. By exploiting the low-rank structure of the ENF signal from the audio spectrogram, we propose an approach based on robust principle component analysis to get rid of the interference from speech contents and some of the background noise, which in our case can be regarded as sparse in nature. Weighted linear prediction is enforced on the low-rank signal subspace to gain accurate ENF estimation. The performance of the proposed scheme is analyzed and evaluated as a function of SNR, and the Cramér-Rao Lower Bound (CRLB) is approached at an SNR level above -10 dB. Experiments on real datasets have demonstrated the advantages of the proposed method over state-of-the-art work in terms of estimation accuracy. Specifically, the proposed scheme can effectively capture the ENF fluctuations along the time axis using small numbers of signal observations while preserving sufficient frequency precision.

References

  1. Catalin Grigoras. 2005. Digital audio recording analysis: The electric network frequency (ENF) criterion. International Journal of Speech Language 8 the Law 12, 1, 63--76.Google ScholarGoogle Scholar
  2. C. Grigoras. 2007. Applications of ENF criterion in forensic audio, video, computer and telecommunication analysis. Forensic Science International 167, 2--3, 136--145.Google ScholarGoogle ScholarCross RefCross Ref
  3. Robert C. Maher. 2009. Audio forensic examination. IEEE Signal Processing Magazine 26, 2, 84--94.Google ScholarGoogle ScholarCross RefCross Ref
  4. Richard W. Sanders. 2008. Digital audio authenticity using the electric network frequency. In Proceedings of the 33rd International Conference on Audio Forensics-Theory and Practice.Google ScholarGoogle Scholar
  5. Daniel Patricio Nicolalde Rodríguez, José Antonio Apolinário, and Luiz Wagner Pereira Biscainho. 2010. Audio authenticity: Detecting ENF discontinuity with high precision phase analysis. IEEE Transactions on Information Forensics 8 Security 5, 3, 534--543. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Paulo Antonio Andrade Esquef, José Antonio Apolinário, and Luiz W. P. Biscainho. 2014. Edit detection in speech recordings via instantaneous electric network frequency variations. IEEE Transactions on Information Forensics 8 Security 9, 12, 2314--2326. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Paulo Max Gil Innocencio Reis, Ricardo Kehrle Miranda, and Giovanni Del Galdo. 2017. Esprit-Hilbert-based audio tampering detection with SVM classifier for forensic analysis via electrical network frequency. IEEE Transactions on Information Forensics 8 Security 12, 4, 853--864. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Ravi Garg, Avinash L. Varna, Adi Hajj-Ahmad, and Min Wu. 2013. “Seeing” ENF: Power-signature-based timestamp for digital multimedia via optical sensing and signal processing. IEEE Transactions on Information Forensics 8 Security 8, 9, 1417--1432. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Ravi Garg, Avinash L. Varna, and Min Wu. 2012. Modeling and analysis of electric network frequency signal for timestamp verification. In IEEE International Workshop on Information Forensics 8 Security. 67--72.Google ScholarGoogle ScholarCross RefCross Ref
  10. Maarten Huijbregtse and Zeno Geradts. 2009. Using the ENF criterion for determining the time of recording of short digital audio recordings. In International Workshop on Computational Forensics. 116--124. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. A. Hajj-Ahmad, R. Garg, and M. Wu. 2015. ENF-based region-of-recording identification for media signals. IEEE Transactions on Information Forensics 8 Security 10, 6, 1125--1136.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Hui Su, Adi Hajj-Ahmad, Min Wu, and Douglas W. Oard. 2014. Exploring the use of ENF for multimedia synchronization. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, Florence, Italy, 4613--4617.Google ScholarGoogle Scholar
  13. Saffet Vatansever, Ahmet Emir Dirik, and Nasir Memon. 2017. Detecting the presence of ENF signal in digital videos: a superpixel based approach. IEEE Signal Processing Letters PP(99), 1--1.Google ScholarGoogle Scholar
  14. Adi Hajj-Ahmad, Andrew Berkovich, and Min Wu. 2016. Exploiting power signatures for camera forensics. IEEE Signal Processing Letters 23, 5, 713--717.Google ScholarGoogle ScholarCross RefCross Ref
  15. Ling Fu, Penn N. Markham, Richard W. Conners, and Yilu Liu. 2013. An improved discrete Fourier transform-based algorithm for electric network frequency extraction. IEEE Transactions on Information Forensics 8 Security 8, 7, 1173--1181. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Ralph O. Schmidt. 1986. Multiple emitter location and signal parameter estimation. IEEE Transactionson Antennas 8 Propagation 34, 3, 276--280.Google ScholarGoogle ScholarCross RefCross Ref
  17. R. Roy and T. Kailath. 2002. Esprit-estimation of signal parameters via rotational invariance techniques. IEEE Transactions on Acoustics Speech 8 Signal Processing 37, 7, 984--995.Google ScholarGoogle ScholarCross RefCross Ref
  18. Ode Ojowu, Johan Karlsson, Jian Li, and Yilu Liu. 2012. ENF extraction from digital recordings using adaptive techniques and frequency tracking. IEEE Transactions on Information Forensics 8 Security 7, 4, 1330--1338. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. D. Bykhovsky and A. Cohen. 2013. Electrical network frequency (ENF) maximum-likelihood estimation via a multitone harmonic model. IEEE Transactions on Information Forensics 8 Security 8, 5, 744--753. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. D. C. Rife and R. R. Boorstyn. 2003. Single tone parameter estimation from discrete-time observations. IEEE Transactions on Information Theory 20, 5, 591--598. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Emmanuel J. Candes, Xiaodong Li, Yi Ma, and John Wright. 2009. Robust principal component analysis? Journal of the ACM 58, 3, 11.Google ScholarGoogle Scholar
  22. Zhouchen Lin, Minming Chen, and Yi Ma. 2009. The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. Eprint Arxiv, 9.Google ScholarGoogle Scholar
  23. P. Handel. 2011. Markov-based single-tone frequency estimation. IEEE Transactions on Circuits 8 Systems II Analog 8 Digital Signal Processing 45, 2, 230--232.Google ScholarGoogle ScholarCross RefCross Ref
  24. Adi Hajj-Ahmad, Ravi Garg, and Min Wu. 2013. Spectrum combining for ENF signal estimation. IEEE Signal Processing Letters 20, 9, 883--886.Google ScholarGoogle ScholarCross RefCross Ref

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  1. Robust Electric Network Frequency Estimation with Rank Reduction and Linear Prediction

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    • Published in

      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 14, Issue 4
      Special Section on Deep Learning for Intelligent Multimedia Analytics
      November 2018
      221 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/3282485
      Issue’s Table of Contents

      Copyright © 2018 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 23 October 2018
      • Revised: 1 July 2018
      • Accepted: 1 July 2018
      • Received: 1 January 2018
      Published in tomm Volume 14, Issue 4

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