Skip to main content
Log in

Construction of a Morlet Wavelet Power Spectrum

  • Published:
Multidimensional Systems and Signal Processing Aims and scope Submit manuscript

Abstract

The Morlet wavelets transform (MWT) is an efficient means of detecting and analyzing transient signals. However, ordinary iterative processes that calculate the MWT are time-consuming. In addition, when the MWT is applied to construct a wavelet power spectrum on a linear frequency axis, the peak response appears at a value lower than the actual signal frequency. In this work, formulae that produce a fast MWT and Morlet power spectrum (MPS) scheme without iterative processes are derived. Also, we discuss in detail why the frequency slant phenomenon occurs. To avert this phenomenon, the transform kernel of the MWT is modified to facilitate the construction of an equal-amplitude Morlet wavelet transform. The modified Morlet power spectrum produces the peak responses roughly proportional to the squared input amplitudes at the accurate signal component frequencies.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. J. G. Proakis and D. G. Manolakis, Digital Signal Processing: Principles, Algorithms, and Applications, Macmillan, Inc., 1992, pp. 152-212.

  2. N. Hess-Nielsen and M.V. Wickerhauser, “Wavelets and Time-Frequency Analysis, ” Proceedings of the IEEE, vol. 84, no. 4, April 1996, pp. 523-540.

    Google Scholar 

  3. H. C. Shyu and Y. S. Sun, “Underwater Acoustic Signal Analysis by Multi-Scaling and Multi-Translation Wavelets, ” Proceedings of SPIE, Wavelet Applications V, vol. 3391, Orlando FL. USA, April 1998, pp. 628-636.

    Google Scholar 

  4. E. P. Serrano and M. A. Fabio, “Application of The Wavelet Transform to Acoustic Emission Signals Processing, ” IEEE Transactions on Signal Processing, vol. 44, no. 5, May 1996, pp. 1270-1276.

    Google Scholar 

  5. P. Guillemain and R. Kronland-Martinet, “Characterization of Acoustic Signals through Continuous Linear Time-Frequency Representations, ” Proceedings of the IEEE, vol. 84, no. 4, April 1996, pp. 561-585.

    Google Scholar 

  6. V. Perrier, T. Philipovitch, and C. Basdevant, “Wavelet Spectra Compared to Fourier Spectra, ” J. Math. Phys. vol. 36, no. 3, March 1995, pp. 1506-1519.

    Google Scholar 

  7. I. Daubechies,Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics, Philadelphia, Pennsylvania, 1992.

  8. M. V. Wickerhauser, Adapted Wavelet Analysis from Theory to Software, Wellesley, MA: A K Peters Ltd., 1994.

    Google Scholar 

  9. A. Grossman, R. Kronland-Martinet, and J. Morlet, “Reading and Understanding Continuous Wavelet Transforms, ” in Wavelets, Time-Frequency Methods and Phase Space, Marseille, France: Springer, 1989, pp. 2-20.

    Google Scholar 

  10. A. Cohen and J. Kovacevic, “Wavelets: The Mathematical Background, ” Proceedings of the IEEE, vol. 84, no. 4, April 1996, pp. 514-522.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shyu, HC., Sun, YS. Construction of a Morlet Wavelet Power Spectrum. Multidimensional Systems and Signal Processing 13, 101–111 (2002). https://doi.org/10.1023/A:1013847512432

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1013847512432

Navigation