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Authenticity identification of speaker digital recording data based on quantum genetic algorithm

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Abstract

The problem we are trying to solve in this paper is the authenticity identification of speaker digital recording data. As an important basis of the judicial identification, it is crucial to ensure the authenticity of digital recording data. A large number of solutions have been proposed to address the problem. However, classic methods are usually based on logical symbol rather than the physical detection of energy or phase, and these solutions show drawbacks in terms of identification inefficiency, algorithm instability and heavy time overhead. In this paper, we propose to utilize the quantum theory to address the problem. Any tampering operation for digital recording data can lead to the change of charge in the memory, and it can utilize the subtle change to implement the identification. First, we analyze the quantum nature of storage and investigate to extract the transmittance of speech signal as the characteristic value through quantum tunneling theory. Second, aiming at the characteristics of speech signal, we utilize the transmittance to define the rotation angle step function and propose an improved quantum genetic algorithm to detect the change of phase. The proposed method achieves the authenticity identification based on phase detection. The results obtained in this research include the problem can be addressed by phase detection solution based on quantum genetic algorithm, and it shows performance benefits compared with existing solutions by simulation experiment. It is not only theoretically but also practically feasible to realize authenticity identification of speak digital recording data.

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References

  1. Bao Y (2011) Electric network frequency estimation based on fractional fourier transform for audio authenticity. Tech Acoust 30(4):209–211

    Google Scholar 

  2. Brixen EB (2007) Further investigation into the ENF criterion for forensic authentication. 123rd audio engineering society convention

  3. Cooper AJ (2010) An automated approach to the electric network frequency (ENF) criterion-theory and practice. Forensic Linguistics 16(2):193–218

    MathSciNet  Google Scholar 

  4. Cui J, Liu Y, Xu Y et al (2013) Tracking generic human motion via fusion of low-and high-dimensional approaches. IEEE Trans Syst Man Cybern B 43(4):996–1002

    Article  Google Scholar 

  5. Daniel R (2010) Audio authenticity. Detecting ENF discontinuity with high precision phase analysis. IEEE Trans Inf Forensics Secur 5(3):534–543

    Article  Google Scholar 

  6. Deng F, Bao C (2016) Speech enhancement based on AR model parameters estimation. Speech Comm 79(c):30–46

    Article  Google Scholar 

  7. Farid H (1999) Detecting digital forgeries using bispectral analysis. MIT AI memo AIM-1657, MIT

  8. Feng A, Su H (2011) Improved quantum genetic algorithm and its application. Comput Eng 37(5):199–201

    Google Scholar 

  9. Griffiths DJ (2011) Introduction to quantum mechanics, 2nd edn. China Machine Press, Beijing

    Google Scholar 

  10. Grigoras C (2005) Digital audio recording analysis: the electric network frequency (ENF) criterion. International Journal of Speech Language and the Law 12(1):63–67

    Article  Google Scholar 

  11. Guo H (2010) Research on authenticity analysis of audio evidence. Telecommunications Science 11(A):56–60

    Google Scholar 

  12. Han KH, Kim JH (2000) Genetic quantum algorithm and its application to combinatorial optimization problem. In: Process of IEEE congress on evolutionary computation, pp 1354–1360

  13. He Z, Pan P (2011) An improved method of MFCC parameter extraction in speaker recognition. Science Technology and Engineering 11(18):4215–4218

    Google Scholar 

  14. Kajstura M, Trawinska A, Hebenstreit J (2006) Application of the electrical network frequency (ENF) criterion. a case of a digital recording. Forensic Sci Int 155(2-3):165–171

    Article  Google Scholar 

  15. Koenig BE, Lacey DS (2009) Forensic authentication of digital audio recordings. J Audio Eng Soc 57(9):662–695

    Google Scholar 

  16. Lai L, Jin F, Wu H (2015) Speech de-noising method using quantum stochastic filter. Inf Control 44(5):598–603

    Google Scholar 

  17. Liu F, Chen Z (2014) Frequency estimation based on Rife algorithm and its implementation on FPGA. Industrial Control Computer 27(4):89–92

    Google Scholar 

  18. Liu L, Cheng L, Liu Y et al (2016) Recognizing complex activities by a probabilistic interval-based model. In: Thirtieth AAAI conference on artificial intelligence, pp 1266–1272

  19. Liu Y (1999) A fast and accurate single frequency estimator synthetic approach. Acta Electron Sin 27(6):126–128

    Google Scholar 

  20. Liu Y, Zhang X, Cui J et al (2010) Visual analysis of child-adult interactive behaviors in video sequences. In: International conference on virtual systems and multimedia, pp 26–33

  21. Liu Y, Cui J, Zhao H et al (2012) Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking. In: International conference on pattern recognition, pp 898–901

  22. Liu Y, Yao C, Sun C et al (2013) Authentication of digital audio recording based on power system frequency. Chin J Sci Instrum 34(6):1434–1440

    Google Scholar 

  23. Liu Y, Nie L, Han L et al (2015) Action2activity: recognizing complex activities from sensor data. In: Proceedings of the international joint conference on artificial intelligence. IJCAI, pp 1617–1623

  24. Liu Y, Nie L, Liu L et al (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115

    Article  Google Scholar 

  25. Liu Y, Zhang L, Nie L et al (2016) Fortune teller: predicting your career path. In: Proceedings of the AAAI conference on artificial intelligence. AAAI, pp 201–207

  26. Liu Y, Zheng Y, Liang Y et al (2016) Urban water quality prediction based on multi-task multi-view learning. In: Proceedings of the international joint conference on artificial intelligence. IJCAI, pp 2576–2582

  27. Lu Y, Wei Y, Liu Y et al (2017) Towards unsupervised physical activity recognition using smartphone accelerometers. Multimed Tools Appl 76(8):10701–10719

  28. Ma X, Nikias LC (1996) Joint estimation of time delay and frequency delay in impulsive noise using fractional lower order statistics. IEEE Trans Signal Process 44(11):2669–2687

    Article  Google Scholar 

  29. Maher RC (2009) Audio forensic examination: authenticity, enhancement, and interpretation. IEEE Signal Proc Mag 26(2):84–94

    Article  Google Scholar 

  30. Narayana A, Moore M (2002) Quantum-inspired genetic algorithms. In: IEEE international conference on evolutionary computation, pp 61–66

  31. Nielsen M, Chuang I (2003) Quantum computation and quantum information. Higher Education Press, Beijing

    MATH  Google Scholar 

  32. Pan P, He Z (2012) Method of speaker feature parameter extraction based on duffing stochastic resonance. Comput Eng Appl 48(35):123–125

    Google Scholar 

  33. Pan P, Luo H, Li H (2012) Voice authenticity detection method on stochastic resonance. Comput Eng 39(5):277–230

    Google Scholar 

  34. Preotiuc-Pietro D, Liu Y, Hopkins D et al (2017) Beyond binary labels: political ideology prediction of twitter users. In: Annual meeting of the association for computational linguistics, pp 729–740

  35. Qian G, Huang D (2012) Quantum genetic algorithm based on angle coding of 3D. Computer Science 39(8):242–245

    Google Scholar 

  36. Song X, Wang S, Niu X (2014) Survey of problem in quantum image processing. Intelligent Computer and Applications 4(6):11–14

    Google Scholar 

  37. Tian G, Pan P, Liu Q (2016) The authenticity of digital recording data identification based on quantum tunnelling effect. Computer Knowledge and Technology 12(6):197–200

    Google Scholar 

  38. Wang L, Zhu J (2009) Method for modifying mandarin speech based on TD-PSOLA. Electronic Measurement Technology 32(12):74–76

    Google Scholar 

  39. Wang Y, Li Y (2007) A novel quantum genetic algorithm for TSP. Chinese Journal of Computer 30(5):748–755

    MathSciNet  Google Scholar 

  40. Wang Y, Pan P, Tian G (2015) A method of speaker feature extraction based on quantum well group. Science Technology and Engineering 15(2):267–272

    Google Scholar 

  41. Xie Q, Wei X, Luo K et al (2009) Packet loss concealment algorithm in voIP. Comput Eng 35(5):246–248

    Google Scholar 

  42. Yan C, Zhang Y, Dai F et al (2014) Efficient parallel framework for HEVC motion estimation on many-core Processors. Electronics 50(11):805–806

    Google Scholar 

  43. Yan C, Zhang Y, Dai F et al (2014) Parallel deblocking filter for HEVC on many-core processor. Electron Lett 50(5):367–368

    Article  Google Scholar 

  44. Yan C, Zhang Y, Xu J et al (2014) Efficient parallel HEVC intra prediction on many-core processor. IEEE Trans Circuits Syst Video Technol 24(12):2077–2089

    Article  Google Scholar 

  45. Yan C, Zhang Y, Xu J et al (2014) A highly parallel framework for HEVC coding unit partitioning tree decision on many-core processors. IEEE Signal Process Lett 21(5):573–576

    Article  Google Scholar 

  46. Yan C, Xie H, Liu S et al (2017) Effective uyghur language text detection in complex background images for traffic prompt identification. In: IEEE transactions on intelligent transportation systems

  47. Yan C, Xie H, Yang D et al (2017) Supervised hash coding with deep neural network for environment perception of intelligent vehicles. In: IEEE transactions on intelligent transportation systems

  48. Yang R, Qu Z, Huang J (2008) Detecting digital audio forgeries by checking frame offsets. In: Workshop on multimedia and security, pp 21–26

  49. Yao Q, Chai P, Xuan G et al (2006) Audio re-sampling detection in audio forensics based on EM algorithm. Journal of Computer Applications 26(11):2598–2601

    Google Scholar 

  50. Yu J, Zhang R (2009) Speaker recognition method using MFCC and LPCC features. Computer Engineering and Design 30(5):1189–1191

    Google Scholar 

  51. Zhang X, Sui G, Zheng R et al (2013) An improved quantum genetic algorithm of quantum revolving gate. Comput Eng 39(4):234–238

    Google Scholar 

  52. Zhang Z (2010) Novel improved quantum genetic algorithm. Comput Eng 36(6):181–183

    Google Scholar 

  53. Zhou Z, Huang Y, Zhang Y et al (2005) Recent advancement in research of quantum computation. Prog Phys 25(4):368–385

    Google Scholar 

  54. Zhu Z, Zhang H, Zhu Y (2009) Semi-conductor integrated circuits, 2nd edn. Tsinghua University Press, Beijing

    Google Scholar 

Download references

Acknowledgements

This work reported in this paper is supported by the Natural Science Foundation of Guizhou Province of China under Grant [2012]2132 and Natural Science Foundation of Education Department of Guizhou Province of China under Grant (2015)367.

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Authors and Affiliations

  1. College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China

    Chao Zhou, Ping Pan & Liang Huang

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  1. Chao Zhou
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  2. Ping Pan
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  3. Liang Huang
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Correspondence to Ping Pan.

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Zhou, C., Pan, P. & Huang, L. Authenticity identification of speaker digital recording data based on quantum genetic algorithm. Multimed Tools Appl 77, 19399–19413 (2018). https://doi.org/10.1007/s11042-017-5369-3

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  • DOI: https://doi.org/10.1007/s11042-017-5369-3

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