Skip to main content
SpringerLink
Log in

Exploiting Nonlinearity in Adaptive Signal Processing

  • Conference paper
Advances in Nonlinear Speech Processing (NOLISP 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4885))

Included in the following conference series:

  • 586 Accesses

Abstract

Quantitative performance criteria for the analysis of machine learning architectures and algorithms have been long established. However, the qualitative performance criteria, e.g., nonlinearity assessment, are still emerging. To that end, we employ some recent developments in signal characterisation and derive criteria for the assessment of the changes in the nature of the processed signal. In addition, we also propose a novel online method for tracking the system nonlinearity. A comprehensive set of simulations in both the linear and nonlinear settings and their combination supports the analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Schreiber, T.: Interdisciplinary application of nonlinear time series methods. Phys. Rep. 308(1), 1–64 (1999)

    Article  MathSciNet  Google Scholar 

  2. Gautama, T., Mandic, D.P., Van Hulle, M.M.: The delay vector variance method for detecting determinism and nonlinearity in time series. Physica D 190(3–4), 167–176 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  3. Gautama, T., Mandic, D., Van Hulle, M.: Signal nonlinearity in fMRI: A comparison between BOLD and MION. IEEE Trans. Med. Imaging 22(5), 636–644 (2003)

    Article  Google Scholar 

  4. Schreiber, T., Schmitz, A.: On the discrimination power of measures for nonlinearity in a time series. Phys. Rev. E 55(5), 5443–5447 (1997)

    Article  Google Scholar 

  5. Gautama, T., Mandic, D.P., Van Hulle, M.M.: On the characterisaion of the deterministic/stochastic and linear/nonlinear nature of time series. Technical Report DPM-04-5, Imperial College London (2004)

    Google Scholar 

  6. Schreiber, T., Schmitz, A.: Surrogate time series. Physica D 142, 346–382 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  7. Theiler, J., Eubank, S., Longtin, A., Galdrikian, B., Farmer, J.: Testing for nonlinearity in time series: The method of surrogate data. Physica D 58, 77–94 (1992)

    Article  Google Scholar 

  8. Schreiber, T., Schmitz, A.: Improved surrogate data for nonlinearity tests. Phys. Rev. Lett., 635–638 (1996)

    Google Scholar 

  9. Weigend, A.S., Gershenfeld, N.A.: Time Series Prediction: Forecasting the Future and Understanding the Past. Addison-Wesley, Reading, MA (1993)

    Google Scholar 

  10. Kaplan, D.: Exceptional events as evidence for determinism. Physica D 73(1), 38–48 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  11. Cao, L.: Practical method for determining the minimum embedding dimension of a scalar time series. Physica D: Nonlinear Phenomena 110(1-2), 43–50 (1997)

    Article  MATH  Google Scholar 

  12. Haykin, S., Li, L.: Nonlinear adaptive prediction of nonstationary signals. IEEE Transactions on Signal Processing 43(2), 526–535 (1995)

    Article  Google Scholar 

  13. Mandic, D.P., Chambers, J.A.: Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability. John Wiley & Sons, Chichester (2001)

    Google Scholar 

  14. Narendra, K.S., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks 1(1), 4–27 (1990)

    Article  Google Scholar 

  15. Mizuta, H., Jibu, M., Yana, K.: Adaptive estimation of the degree of system nonlinearity. In: IEEE Adaptive Systems for Signal Processing and Control Symposium (AS-SPCC), pp. 352–356 (2000)

    Google Scholar 

  16. Cichocki, A., Unbehauen, R.: Neural networks for optimization and signal processing. Wiley, Chichester (1993)

    MATH  Google Scholar 

  17. Figueras-Vidal, A.R., Arenas-Garcia, J., Sayed, A.H.: Steady state performance of convex combinations of adaptive filters. In: ICASSP 2005. Proceedings of the International Conference on Acoustis, Speech and Signal Processing, pp. 33–36 (2005)

    Google Scholar 

  18. Kozat, S.S., Singer, A.C.: Multi-stage adaptive signal processing algorithms. In: Proceedings of the 2000 IEEE Sensor Array and Multichannel Signal Processing Workshop, pp. 380–384 (2000)

    Google Scholar 

  19. Mandic, D.P., Vayanos, P., Boukis, C., Goh, S.L., Jelfs, B., Gautama, T., Rutkowski, T.: Collaborative adaptive learning using hybrid filters. In: Proceedings of ICASSP 2007, vol. III, pp. 921–924 (2007)

    Google Scholar 

  20. Mandic, D.P.: NNGD algorithm for neural adaptive filters. Electronics Letters 36(9), 845–846 (2000)

    Article  Google Scholar 

  21. Schroeter, J., Sondhi, M.: Speech coding based on physiological models of speech production. In: Furui, S., Sondhi, M. (eds.) Advances in speech Signal Processing, pp. 231–268. Marcel Dekker, New York, NY, USA (1992)

    Google Scholar 

  22. Thyssen, J., Nielsen, H., Hansen, S.: Non-linear short-term prediction in speech coding. In: ICASSP 1994. Proceedings of the International Conference on Acoustics, Speech and Signal Processing, vol. 1, pp. 185–188 (1994)

    Google Scholar 

  23. Deller, J.R., Proakis, J.G., Hansen, H.L.: Discrete Time Processing of speech Signals. Prentice-Hall, Englewood Cliffs (1987)

    Google Scholar 

  24. Rabiner, L., Juang, B.H.: Fundamentals of speech recognition. Prentice-Hall, Englewood Cliffs (1993)

    Google Scholar 

  25. Rabiner, L., Schafer, R.W.: Digital Processing of Speech Signals. Prentice-Hall, Englewood Cliffs (1978)

    Google Scholar 

  26. Kubin, G.: Nonlinear processing of speech. In: Kleijn, W., Paliwal, K. (eds.) Speech coding and synthesis, pp. 557–610. Elsevier Science B.V., Amsterdam (1995)

    Google Scholar 

  27. Banbrook, M., McLaughlin, S., Mann, I.: Speech characterisation and synthesis by nonlinear methods. In: Proceedings of the International Conference on Speech and Audio Processing, vol. 7, pp. 1–17 (1999)

    Google Scholar 

  28. Martinez, F., Guillamon, A., Alcaraz, J., Alcaraz, M.: Detection of chaotic behaviour in speech signals using the largest lyapunov exponent. In: DSP 2002. IEEE International Conference on Digital Signal Processing, pp. 317–320 (2002)

    Google Scholar 

  29. Miyano, T., Nagami, A., Tokuda, I., Aihara, K.: Detecting nonlinear determinism in voiced sounds of japanese vowel /a/. International Journal of Bifurcation and Chaos 10(8), 1973–1979 (2000)

    Article  Google Scholar 

  30. Townshend, B.: Nonlinear prediction of speech. In: ICASSP 1991. Proceedings of the International Conference on Acoustics, Speech and Signal Processing, pp. 425–428 (1991)

    Google Scholar 

  31. Hansen, J., Gavidia-Ceballos, L., Kaiser, J.: A nonlinear operator-based speech feature analysis method with applications to vocal fold pathology assessment. IEEE Transactions on Biomedical Engineering 45(3), 300–313 (1998)

    Article  Google Scholar 

  32. Maragos, P., Quatieri, T., Kaiser, J.: Speech nonlinearities, modulations, and energy operators. In: ICASSP 1991. Proceedings of the International Conference on Acoustics, Speech and Signal Processing, pp. 421–424 (1991)

    Google Scholar 

  33. Wokurek, W.: Time-frequency analysis of the glottal opening. In: ICASSP 1997. Proceedings of the International Conference on Acoustics, Speech and Signal Processing, pp. 1435–1438 (1997)

    Google Scholar 

  34. Turunen, J., Tanttu, J.T., Loula, P.: Hammerstein model for speech coding. EURASIP Journal on Applied Signal Processing 2003(12), 1238–1249 (2003)

    Article  MATH  Google Scholar 

  35. Mandic, D.P., Baltersee, J., Chambers, J.A.: Nonlinear prediction of speech with a pipelined recurrent neural network and advanced learning algorithms. In: Prochazka, A., Uhlir, J., Rayner, P.J.W., Kingsbury, N.G. (eds.) Signal Analysis and Prediction, pp. 291–309. Birkhauser, Boston (1998)

    Google Scholar 

  36. Lindsey, G., Breen, A., Nevard, S.: Spar’s archivable actual-word databases (1987)

    Google Scholar 

  37. Birgmeier, M., Bernhard, H.P., Kubin, G.: Nonlinear long-term prediction of speech signals. In: ICASSP 1997. IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 1283–1286 (1997)

    Google Scholar 

  38. Jelfs, B., Mandic, D.P.: Toward online monitoring of the changes in signal modality: The degree of sparsity. In: Proceedings of the 7th IMA International Conference on Mathematics for Signal Processing, pp. 29–32 (2006)

    Google Scholar 

  39. Brookes, M., Gudnasonand, J., Kounoudes, A., Naylor, P.: Estimation of glottal closure instants in voiced speech using the DYPSA algorithm. IEEE Transactions on Audio, Speech and Language Processing 15(1), 34–43 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK

    Phebe Vayanos, Mo Chen, Beth Jelfs & Danilo P. Mandic

Authors
  1. Phebe Vayanos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mo Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Beth Jelfs
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Danilo P. Mandic
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Mohamed Chetouani Amir Hussain Bruno Gas Maurice Milgram Jean-Luc Zarader

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Vayanos, P., Chen, M., Jelfs, B., Mandic, D.P. (2007). Exploiting Nonlinearity in Adaptive Signal Processing. In: Chetouani, M., Hussain, A., Gas, B., Milgram, M., Zarader, JL. (eds) Advances in Nonlinear Speech Processing. NOLISP 2007. Lecture Notes in Computer Science(), vol 4885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77347-4_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-77347-4_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77346-7

  • Online ISBN: 978-3-540-77347-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation