Estimating Stochasticity of Acoustic Signals

  • Sergei Aleinik
  • Oleg Kudashev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8773)

Abstract

In this paper, known methods for estimating the stochasticity of acoustic signals are compared, along with a new method based on adaptive signal filtration. Statistical simulation shows that the described method has better characteristics (lower variance and bias) than the other stochasticity measures. The parameters of the method, and their influence on performance, are investigated. Practical implementations for using the method are considered.

Keywords

Stochasticity spectral entropy linear prediction event detection 

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References

  1. 1.
    Misra, H., Ikbal, S., Sivadas, S., Bourlard, H.: Multi-resolution Spectral Entropy Feature for Robust ASR. In: Proc. ICASSP, vol. 1, pp. 253–256 (2005)Google Scholar
  2. 2.
    Toh, A.M., Togneri, R., Nordholm, S.: Spectral entropy as speech features for speech recognition. In: Proc. PEECS, pp. 22–25 (2005)Google Scholar
  3. 3.
    Bardeli, R.: Source Separation Using the Spectral Flatness Measure. In: Proc. Machine Listening in Multisource Environments, pp. 80–85 (2011)Google Scholar
  4. 4.
    Bachu, R.G., Kopparthi, S., Adapa, B., Barkana, B.D.: Separation of Voiced and Unvoiced Using Zero Crossing Rate and Energy of the Speech Signal. In: Proc. American Society for Engineering Education, pp. 1–7 (2008)Google Scholar
  5. 5.
    Khan, A.U., Bhaiya, L.P., Banchhor, S.K.: Hindi Speaking Person Identification Using Zero Crossing Rate. Int. J. of Soft Computing and Engineering 2(3), 101–104 (2012)Google Scholar
  6. 6.
    Madhu, N.: Note on Measures for Spectral Flatness. Electronics Letters 23, 1195–1196 (2009)CrossRefGoogle Scholar
  7. 7.
    Dubnov, S.: Non-gaussian source-filter and independent components generalizations of spectral flatness measure. In: Proc. of the 4th International Conference on Independent Components Analysis, pp. 143–148 (2003)Google Scholar
  8. 8.
    Aleinik, S.: Time series determinancy evaluation. Radiotekhnika 9, 16–22 (1999)Google Scholar
  9. 9.
    Widrow, B., Lehr, M., Beaufays, F., Wan, E., Bileillo, M.: Learning algorithms for adaptive processing and control. In: IEEE International Conference on Neural Networks, vol. 1, pp. 1–8 (1993)Google Scholar
  10. 10.
    Puente, C.E., Obregón, N., Sivakumar, B.: Chaos and stochasticity in deterministically generated multifractal measures. Fractals 10(1), 91–102 (2002)CrossRefGoogle Scholar
  11. 11.
    Sivakumar, B.: Is a Chaotic Multi-Fractal Approach for Rainfall Possible? Hydrological Processes 15(6), 943–955 (2001)CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Heim, A., Sorger, U., Hug, F.: Doppler-variant modeling of the vocal tract. In: Proc. ICASSP-2008, pp. 4197–4200 (2008)Google Scholar
  14. 14.
    Corneliu, M., Costinescu, B.: Implementing the Levinson-Durbin Algorithm on the SC140. Freescale Semiconductor (AN2197), Rev. 1, 1 (2005)Google Scholar
  15. 15.
    Bitzer, J., Brandt, M.: Speech Enhancement by Adaptive Noise Cancellation: Problems, Algorithms and Limits. In: Proc. AES–39, pp. 106–113 (2010)Google Scholar
  16. 16.
    Orfanidis, S.J.: Introduction to Signal Processing, http://www.ece.rutgers.edu/~orfanidi/intro2sp/orfanidis-i2sp.pdf
  17. 17.
    Haykin, S.: Adaptive Filter Theory. Englewood Cliffs, Prentice-Hall (1996) Google Scholar
  18. 18.
    Ignatov, P., Stolbov, M., Aleinik, S.: Semi-Automated Technique for Noisy Recording Enhancement Using an Independent Reference Recording. In: Proc. 46th International Conference of the Audio Engineering Society, pp. 57–64 (2012)Google Scholar
  19. 19.
    Varga, A., Steeneken, H.J.M.: Assessment for automatic speech recognition II: NOISEX-92: a database and an experiment to study the effect of additive noise on speech recognition systems. Speech Communication 12(3), 247–251 (1993)CrossRefGoogle Scholar
  20. 20.
    Kozlov, A., Kudashev, O., Matveev, Y., Pekhovsky, T., Simonchik, K., Shulipa, A.: SVID speaker recognition system for NIST SRE 2012. In: Železný, M., Habernal, I., Ronzhin, A. (eds.) SPECOM 2013. LNCS, vol. 8113, pp. 278–285. Springer, Heidelberg (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sergei Aleinik
    • 1
    • 2
  • Oleg Kudashev
    • 2
  1. 1.Speech Technology CenterSt. PetersburgRussia
  2. 2.ITMO UniversitySt. PetersburgRussia

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