ABSTRACT
The Electric Network Frequency (ENF) criterion provides useful forensic evidence for multimedia authentication. In this paper, a systematic study of non-parametric and parametric spectral estimation methods is conducted for ENF extraction. Fast implementations of the Capon method and the Iterative Adaptive Approach, which exploit the Gohberg-Semencul factorization of the inverse covariance matrix, are included as well. When long segments are used, a very high matching accuracy is achieved. That is, the maximum correlation-coefficient between the extracted ENF and the ground truth may exceed 99%. Similarly, the standard deviation of error maybe as small as 1.069 · 10-3. Non-parametric spectral estimation techniques are shown to be able to detect an alteration in an audio recording, where a short utterance recorded in Europe is replaced by the same content recorded in the US.
- D. Bykhovsky and A. Cohen. 2013. Electrical network frequency (ENF) maximum-likelihood estimation via a multitone harmonic model. IEEE Trans. Information Forensics and Security 8, 5 (May 2013), 744--753. Google ScholarDigital Library
- A. J. Cooper. 2008. The electric network frequency (ENF) as an aid to authenticating forensic digital audio recordings. An automated approach. In Proc. 33rd AES Int. Conf. Audio Forensics-Theory and Practice.Google Scholar
- R. Garg, A. Hajj-Ahmad, and M. Wu. 2013. Geo-location estimation from Electrical Network Frequency signals. In Proc. 2013 IEEE Int. Conf. Audio, Speech, and Signal Processing. 2862--2866.Google Scholar
- R. Garg, A. L. Varna, and M. Wu. 2011. Seeing ENF: natural time stamp for digital video via optical sensing and signal processing. In Proc. 19th ACM Int. Conf. Multimedia. 23--32. Google ScholarDigital Library
- R. Garg, A. L. Varna, and M. Wu. 2012. Modeling and analysis of electric network frequency signal for timestamp verification. In Proc. 2012 IEEE Int. Workshop Information Forensics and Security. 67--72.Google Scholar
- J. Garofolo. 1988. Getting started with the DARPA TIMIT cd-rom: An acoustic phonetic continuous speech database. Technical Report. National Inst. Standards and Technology (NIST).Google Scholar
- G. O. Glentis and A. Jakobsson. 2011. Efficient implementation of iterative adaptive approach spectral estimation techniques. IEEE Trans. Signal Processing 59, 9 (Sept. 2011), 4154--4167. Google ScholarDigital Library
- G. O. Glentis and A. Jakobsson. 2011. Time-recursive IAA spectral estimation. IEEE Signal Processing Letters 18, 2 (Feb. 2011), 111--114.Google ScholarCross Ref
- C. Grigoras. 2005. Digital audio recording analysis: the electric network frequency criterion. Int. Journal Speech, Language, and the Law 12, 1 (June 2005), 63--76.Google ScholarCross Ref
- C. Grigoras. 2007. Applications of ENF criterion in forensic audio, video, computer and telecommunication analysis. Forensic Science Int. 167, 2 (April 2007), 136 -- 145.Google ScholarCross Ref
- A. Hajj-Ahmad, R. Garg, and M. Wu. 2012. Instantaneous frequency estimation and localization for ENF signals. In Proc. 2012 Asia-Pasific Signal and Information Processing Association Annual Summit and Conf. 1--10.Google Scholar
- M. Huijbregtse and Z. Geradts. 2009. Using the ENF criterion for determining the time of recording of short digital audio recordings. In Proc. Int. Workshop Computational Forensics. 116--124. Google ScholarDigital Library
- C. Kotropoulos and S. Samaras. 2014. Mobile phone brand and model identification using recorded speech signals. In Proc. 19th Int. Conf. Digital Signal Processing. Hong Kong, 586--591.Google Scholar
- D. P. Nicolalde-Rodriguez, J. A. Apolinario, and L. W. P. Biscainho. 2013. Audio authenticity based on the discontinuity of ENF higher harmonics. In Proc. 21st European Signal Processing Conf. 1--5.Google Scholar
- O. Ojowu, J. Karlsson, J. Li, and Y. Liu. 2012. ENF extraction from digital recordings using adaptive techniques and frequency tracking. IEEE Trans. Information Forensics and Security 7, 4 (Aug. 2012), 1330--1338. Google ScholarDigital Library
- T. Rakthanmanon, B. Campana, A. Mueen, G. Batista, B. Westover, Q. Zhu, J. Zakaria, and E. Keogh. 2012. Searching and mining trillions of time series subsequences under dynamic time warping. In Proc. 18th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining. 262--270. Google ScholarDigital Library
- J. O. Smith and X. Serra. 1987. PARSHL: An analysis/synthesis program for non-harmonic sounds based on a sinusoidal representation. CCRMA, Department of Music, Stanford University.Google Scholar
- P. Stoica and R. L. Moses. 2005. Spectral Analysis of Signals. Upper Saddle River, NJ: Pearson Prentice Hall.Google Scholar
- M. Xue, L. Xu, and J. Li. 2011. IAA spectral estimation: Fast implementations using the Gohberg-Semencul factorization. IEEE Trans. Signal Processing 59, 7 (July 2011), 3251--3261. Google ScholarDigital Library
- T. Yardibi, J. Li, P. Stoica, M. Xue, and A. B. Baggeroer. 2010. Source localization and sensing: A nonparametric iterative adaptive approach based on weighted least squares. IEEE Trans. Aerospace Electronic Systems 46, 1 (Jan. 2010), 425--443.Google ScholarCross Ref
Index Terms
- Assessing spectral estimation methods for electric network frequency extraction
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