Special Issue on Underwater AcousticsNo Access

Denoising Underwater Acoustic Signals for Applications in Acoustical Oceanography

Department of Mathematics and Applied Mathematics, University of Crete, Institute of Applied and Computational Mathematics, FORTH, Voutes University Campus, 70013 Heraklion, Crete, Greece

E-mail Address: taroud@uoc.gr

Search for more papers by this author

Costas Smaragdakis

Department of Mathematics and Applied Mathematics, University of Crete, Institute of Applied and Computational Mathematics, FORTH, Voutes University Campus, 70013 Heraklion, Crete, Greece

E-mail Address: kesmarag@tem.uoc.gr

Search for more papers by this author

, and

N. Ross Chapman

School of Earth and Ocean Sciences, University of Victoria, P. O. Box 3055, Victoria, British Columbia, Canada V8W3P6, Canada

E-mail Address: chapman@uvic.ca

Search for more papers by this author

https://doi.org/10.1142/S0218396X17500151Cited by:13 (Source: Crossref)

Abstract

A method for denoising underwater acoustic signals used in applications of acoustical oceanography is presented. The method has been introduced for imaging denoising and has been modified to be applied with acoustic signals. The method keeps the energy significant part of the raw signal and reduces the effects of noise by comparing overlapping signal windows and keeping components which resemble true signal energy. It is shown by means of characteristic experiments in connection with a statistical signal characterization scheme based on wavelet transform, that using the statistical features of the wavelet sub-band coefficients of the denoised signal, tomography or geoacoustic inversions lead to a reliable estimation of the parameters of a marine environment.

Keywords:

References

1. M. Taroudakis, G. Tzagkarakis and P. Tsakalides, Classification of shallow water acoustic signals via alpha stable modeling of the one dimensional wavelet coefficients, J. Acoust. Soc. Am. 119 (2006) 1396–1405. Crossref, Web of Science, Google Scholar
2. G. Tzagkarakis, M. Taroudakis and P. Tsakalides, A statistical geoacoustic inversion scheme based on a modified radial basis functions neural network, J. Acoust. Soc. Am. 122 (2007) 1959–1968. Crossref, Web of Science, Google Scholar
3. M. Taroudakis and C. Smaragdakis, Tomographic and bottom geoacoustic inversions using genetic algorithms and a statistical characterization of the acoustic signal, Acta Acoust. United with Acoust. 95 (2009) 814–822. Crossref, Web of Science, Google Scholar
4. M. Taroudakis and C. Smaragdakis, Inversions of statistical parameters of an acoustic signal in range-dependent environments with applications in ocean acoustic tomography, J. Acoust. Soc. Am. 134 (2013) 2814–2822. Crossref, Web of Science, Google Scholar
5. M. Taroudakis, C. Smaragdakis and N. R. Chapman, Inversion of acoustical data from the Shallow Water 06 experiment by statistical signal characterization, J. Acoust. Soc. Am. 136 (2013) EL336–EL342. Crossref, Web of Science, Google Scholar
6. M. Taroudakis and C. Smaragdakis, Underwater acoustic signal characterization in the presence of noise, in Proc. Internoise 2010, CD-ROM edition (Lisbon, Portugal, 2010). Google Scholar
7. D. Tang et al., A joint acoustic propagation/nonlinear internal wave physics experiment, Oceanogr. 20 (2007) 156–167. Crossref, Web of Science, Google Scholar
8. J. Bonnel and N. R. Chapman, Geoacoustic inversion in a dispersive waveguide using warping operators, J. Acoust. Soc. Am. 130 (2011) EL101–EL107. Crossref, Web of Science, Google Scholar
9. J. Bonnel, S. E. Dosso and N. R. Chapman, Bayesian geoacoustic inversion of single hydrophone light bulb data using warping dispersion analysis, J. Acoust. Soc. Am. 134 (2013) 120–130. Crossref, Web of Science, Google Scholar
10. C. Chui, An Introduction to Wavelets (Academic Press, San Diego, 1992), pp. 215–243. Google Scholar
11. S. Kullback, Information Theory and Statistics (Dover Publications, NY, 1968), pp. 6–7. Google Scholar
12. J. Marial, J. Ponce and G. Sapiro, Online dictionary learning for sparse coding, in Proc. 26th Int. Conf. Machine Learning, Montreal, Canada (2009). Google Scholar
13. M. Jafari and M. D. Plumbley, Fast dictionary learning for sparse representations of speech signals, IEEE J. Signal Process. 5 (2011) 1025–1031. Crossref, Web of Science, Google Scholar
14. C. Ramirez, V. Kreinovich and M. Argaez, Why $ℓ_{1}$ is a good approximation to $ℓ_{0}$ : A geometric explanation, J. Uncertain Syst. 7 (2013) 814–822. Google Scholar
15. N. Parikh and S. Boyd, Proximal algorithms, Found. Trends Optim. 1(3) (2014) 123–231. Google Scholar
16. M. Taroudakis and M. Markaki, On the use of matched-field processing and hybrid algorithms for vertical slice tomography, J. Acoust. Soc. Am. 102 (1997) 885–895. Crossref, Web of Science, Google Scholar