The construction of wavelet network for speech signal processing | Neural Computing and Applications Skip to main content
Springer Nature Link
Log in

The construction of wavelet network for speech signal processing

  • Original Article
  • Published:
Neural Computing & Applications Aims and scope Submit manuscript

Abstract

Wavelet decomposition reconstructs a signal by a series of scaled and translated wavelets. Incorporating discrete wavelet decomposition theory with neural network techniques, wavelet networks have recently emerged as a powerful tool for many applications in the field of signal processing, such as data compression and function approximation. In this paper, four contributions are claimed: (1) From the point of view of machine learning, we analyse and construct wavelet network to achieve the compact representation of a signal. (2) A new algorithm of constructing wavelet network is proposed. The orthogonal least square (OLS) is employed to prune the wavelet network. (3) Our experiments on speech signal processing results show that the wavelet network pruned by OLS achieves the best approximation and prediction capabilities among the representative speech processing techniques. (4) Our proposed methodology has been successfully applied to speech synthesis for a talking head to read web texts.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Gao JB, Harris CJ, Gunn SR (2001) On a class of support vector kernels based on frames in function hilber spaces. Neural Comput 13:1975–1994

    Article  MATH  Google Scholar 

  2. Gorriz JM, Puntonet CG, Salmeron M, de la Rosa JJG (2004) A new model for time-series forecasting using radial basis functions and exogenous data. Neural Comput Appl 13:101–111

    Google Scholar 

  3. Platt J (1991) A resource-allocating network for function interpolation. Neural Comput 3(2):213–225

    Article  MathSciNet  Google Scholar 

  4. Salmeron M, Ortega J, Puntonet CG, Prieto A (2001) Improved RAN sequential prediction using orthogonal techniques. Neurocomputing 41:153–172

    Article  MATH  Google Scholar 

  5. Moulines E, Charpentier F (1990) Pitch synchronous waveform processing techniques for text-to-speech synthesis using diphones. Speech Commun 9:453–467

    Article  Google Scholar 

  6. McAulay RJ, Quatieri TF (1986) Speech analysis/synthesis based on a sinusoidal representation. IEEE Trans Acoust Speech Signal Process 34:744–754

    Article  Google Scholar 

  7. Mallat SG (1989) A theory of multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Machine Intell 11:674–693

    Article  MATH  Google Scholar 

  8. Daubechies I (1990) The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Inf Theory 36:961–1005

    Article  MATH  MathSciNet  Google Scholar 

  9. Mallat SG, Zhong S (1992) Characterization of signals from multiscale edges. IEEE Trans Pattern Anal Machine Intell 14(7):710–732

    Article  Google Scholar 

  10. Zhang Q, Benveniste A (1992) Wavelet network. IEEE Trans Neural Networks 3(6):889–898

    Article  Google Scholar 

  11. Zhang Q (1997) Using wavelet network in nonparametric estimation. IEEE Trans Neural Networks 8(2):227–236

    Article  Google Scholar 

  12. Bishop CM (19991) Improving the generalization properties of radial basis function neural networks. Neural Comput 3(4):579–588

    Article  MathSciNet  Google Scholar 

  13. Chen S, Cowan CF, Grant PM (1991) Orthogonal least squares learning algorithms for radial basis function networks. IEEE Trans Neural Networks 2(2):302–309

    Article  Google Scholar 

  14. Chen S, Chng ES, Alkadhimim K (1996) Regularized orthogonal least squares algorithm for constructing radial basis function networks. Int J Control 64(5):829–837

    Article  MATH  Google Scholar 

  15. Chen S, Wu Y, Luk BL (1999) Combined genetic algorithm optimisation and regularised orthogonal least squares learning for radial basis function networks. IEEE Trans Neural Networks 10(5):1239–1243

    Article  Google Scholar 

  16. Gomm JB, Yu DL (2000) Selecting radial basis function network centers with recursive orthogonal least squares training. IEEE Trans Neural Networks 11:306–314

    Article  Google Scholar 

  17. Akaike H (1969) Fitting autoregressive models for prediction. Ann Inst Stat Math 21:243–347

    Article  MATH  MathSciNet  Google Scholar 

  18. Chen F, Spinko V, Shi D (2005) Real-time lip synchronization using wavelet network. In: Proceedings of International Conference on Cyberworlds, Singapore

  19. Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Networks 10:988–999

    Article  Google Scholar 

  20. Scholkopf B, Sung KK, Burges CJC, Girosi F, Niyogi P, Poggio T, Vapnik V (1997) Comparing support vector machines with gaussian kernels to radial basis function classifiers. IEEE Trans Signal Process 45(11):2758–2765

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    D. Shi, F. Chen & G. S. Ng

  2. School of Information Technology, Charles Sturt University, Bathurst, NSW, 2795, Australia

    J. Gao

Authors
  1. D. Shi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. F. Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. G. S. Ng
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. J. Gao
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to D. Shi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shi, D., Chen, F., Ng, G.S. et al. The construction of wavelet network for speech signal processing. Neural Comput & Applic 15, 217–222 (2006). https://doi.org/10.1007/s00521-005-0016-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-005-0016-8

Keywords