Classification of Non Stationary Signals Using Ben Wavelet and Artificial Neural Networks
The automatic classification of non stationary signals is an important practical goal in several domains. An essential classification task is to allocate the incoming signal to a group associated with the kind of physical phenomena producing it. In this paper, we present a modular system composed by three blocs: 1) Representation, 2) Dimensionality reduction and 3) Classification. The originality of our work consists in the use of a new wavelet called "Ben wavelet" in the representation stage. For the dimensionality reduction, we propose a new algorithm based on the random projection and the principal component analysis.
[1] S. Abeysekera and B. Boashash, "Methods of signal classification using
the images produced by Wigner distribution" Pattern Recognition Letters, 12,
pp. 717-729, 1991.
[2] L. Atlas, J. Droppo and J. McLaughlin, "Optimizing time-frequency
distributions for automatic classification" In the International Society for
Optical Engineering, 1997.
[3] M. Davy, C. Doncarli, and G. F. Boudreaux-Bartels, "Improved
optimization of time-frequency based signal classifiers" IEEE Sig. Proc. Let,
8, pp. 52-57, 2001.
[4] F. Hlawasch and G. F. Boudreaux-Bartels, "Linear and quadratic timefrequency
signal representations" IEEE Sig. Proc. Mag, 9, pp. 21-67, 1992.
[5] A. Papandreou-Suppappola, F. Hlawasch and G. Boudreaux-Bartels,
"Quadratic time-frequency representations with scale covariance and
generalized time-shift covariance: a unified framework for the affine,
hyperbolic and power classes" Digital signal Processing, 8, pp.3-48, 1998.
[6] L. Cohen, "Generalized phase-space distribution functions" J. Math. Phys,
7, pp. 781-786, 1966.
[7] L. Cohen, "Time-frequency analysis" Prentice Hall, 1995.
[8] O. Rioul and P. Flandrin, "Time-scale energy distributions : a general class
extending wavelet transforms" IEEE Trans on Signal Processing, 40, pp.
1746-1757, 1992.
[9] P. Flandrin, "Temps-fréquence", Academic Press, 1998.
[10] F. Hlawasch, A. F. Papandreou-Suppappola and G. Boudreaux-Bartels,
"The power classes of quadratic time-frequency representations : a
generalization of the hyperbolic and affine classes" In 27th Asilomar Conf on
Signals, Systems and computers, Pacific Grove, CA, 1265-1270, 1993.
[11] F. Hlawasch, A. F. Papandreou-Suppappola and G. Boudreaux-Bartels,
"The hyperbolic class of quadratic time-frequency representations. Part II:
Subclasses, intersection with affine and power classes, regularity unitarity"
IEEE Trans on Signal Processing, 45, pp. 303-315, 1997.
[12] A. Papandreou-Suppappola, F. Hlawasch and G. Boudreaux-Bartels,
"Power class time-frequency representations: interference geometry,
smoothing and implementation" In IEEE Symposium on Time-Frequency and
Time-Scale Analysis, Paris, pp.193-196, 1996.
[13] I. Daubechies, "Ten lectures on wavelets", SIAM, Philadelphia, Pa,
1992.
[14] C. Torrence and G. P. Compo, "A practical guide to wavelet analysis"
Bull. Amer. Meteor. Soc, 79, pp. 61-78, 1998.
[15] M. Benbrahim, K. Benjelloun and A. Ibenbrahim, "Discrimination des
signaux sismiques par réseaux de neurones artificiels" In Proc of 3èmes
journées nationales sur les systèmes intelligents: théorie et applications, Rabat,
Morocco, pp. 62-66; 2004.
[16] R. Bellman, "Adaptive control processes: A guided tour" Princeton
University Press, Princeton, 1961.
[17] W.B. Johnson and J. Lindenstrauss, "Extensions of Lipshitz mapping
into Hilbert space" In Conference in modern analysis and probability, volume
26 of Contemporary Mathematics, Amer. Math. Soc, pp. 189-206, 1984.
[18] I. T. Jolliffe, "Principal component analysis", Springer-Verlag, 1986.
[19] J. E. Jackson, "A user's guide to principal components", John Wiley,
New York, 1991.
[20] M. Benbrahim, A. Daoudi, K. Benjelloun and A. Ibenbrahim,
"Discrimination of seismic signals using artificial neural networks" In Proc of
the second world enformatika congress, WEC'05, Istanbul, Turkey, pp 4-7,
2005.
[21] A. Daoudi, M. Benbrahim and K. Benjelloun, "An intelligent system to
classify leaks in water distribution pipes" In Proc of the second world
enformatika congress, WEC'05, Istanbul, Turkey, pp 1-3, 2005.
[1] S. Abeysekera and B. Boashash, "Methods of signal classification using
the images produced by Wigner distribution" Pattern Recognition Letters, 12,
pp. 717-729, 1991.
[2] L. Atlas, J. Droppo and J. McLaughlin, "Optimizing time-frequency
distributions for automatic classification" In the International Society for
Optical Engineering, 1997.
[3] M. Davy, C. Doncarli, and G. F. Boudreaux-Bartels, "Improved
optimization of time-frequency based signal classifiers" IEEE Sig. Proc. Let,
8, pp. 52-57, 2001.
[4] F. Hlawasch and G. F. Boudreaux-Bartels, "Linear and quadratic timefrequency
signal representations" IEEE Sig. Proc. Mag, 9, pp. 21-67, 1992.
[5] A. Papandreou-Suppappola, F. Hlawasch and G. Boudreaux-Bartels,
"Quadratic time-frequency representations with scale covariance and
generalized time-shift covariance: a unified framework for the affine,
hyperbolic and power classes" Digital signal Processing, 8, pp.3-48, 1998.
[6] L. Cohen, "Generalized phase-space distribution functions" J. Math. Phys,
7, pp. 781-786, 1966.
[7] L. Cohen, "Time-frequency analysis" Prentice Hall, 1995.
[8] O. Rioul and P. Flandrin, "Time-scale energy distributions : a general class
extending wavelet transforms" IEEE Trans on Signal Processing, 40, pp.
1746-1757, 1992.
[9] P. Flandrin, "Temps-fréquence", Academic Press, 1998.
[10] F. Hlawasch, A. F. Papandreou-Suppappola and G. Boudreaux-Bartels,
"The power classes of quadratic time-frequency representations : a
generalization of the hyperbolic and affine classes" In 27th Asilomar Conf on
Signals, Systems and computers, Pacific Grove, CA, 1265-1270, 1993.
[11] F. Hlawasch, A. F. Papandreou-Suppappola and G. Boudreaux-Bartels,
"The hyperbolic class of quadratic time-frequency representations. Part II:
Subclasses, intersection with affine and power classes, regularity unitarity"
IEEE Trans on Signal Processing, 45, pp. 303-315, 1997.
[12] A. Papandreou-Suppappola, F. Hlawasch and G. Boudreaux-Bartels,
"Power class time-frequency representations: interference geometry,
smoothing and implementation" In IEEE Symposium on Time-Frequency and
Time-Scale Analysis, Paris, pp.193-196, 1996.
[13] I. Daubechies, "Ten lectures on wavelets", SIAM, Philadelphia, Pa,
1992.
[14] C. Torrence and G. P. Compo, "A practical guide to wavelet analysis"
Bull. Amer. Meteor. Soc, 79, pp. 61-78, 1998.
[15] M. Benbrahim, K. Benjelloun and A. Ibenbrahim, "Discrimination des
signaux sismiques par réseaux de neurones artificiels" In Proc of 3èmes
journées nationales sur les systèmes intelligents: théorie et applications, Rabat,
Morocco, pp. 62-66; 2004.
[16] R. Bellman, "Adaptive control processes: A guided tour" Princeton
University Press, Princeton, 1961.
[17] W.B. Johnson and J. Lindenstrauss, "Extensions of Lipshitz mapping
into Hilbert space" In Conference in modern analysis and probability, volume
26 of Contemporary Mathematics, Amer. Math. Soc, pp. 189-206, 1984.
[18] I. T. Jolliffe, "Principal component analysis", Springer-Verlag, 1986.
[19] J. E. Jackson, "A user's guide to principal components", John Wiley,
New York, 1991.
[20] M. Benbrahim, A. Daoudi, K. Benjelloun and A. Ibenbrahim,
"Discrimination of seismic signals using artificial neural networks" In Proc of
the second world enformatika congress, WEC'05, Istanbul, Turkey, pp 4-7,
2005.
[21] A. Daoudi, M. Benbrahim and K. Benjelloun, "An intelligent system to
classify leaks in water distribution pipes" In Proc of the second world
enformatika congress, WEC'05, Istanbul, Turkey, pp 1-3, 2005.
@article{"International Journal of Information, Control and Computer Sciences:62997", author = "Mohammed Benbrahim and Khalid Benjelloun and Aomar Ibenbrahim and Adil Daoudi", title = "Classification of Non Stationary Signals Using Ben Wavelet and Artificial Neural Networks", abstract = "The automatic classification of non stationary signals is an important practical goal in several domains. An essential classification task is to allocate the incoming signal to a group associated with the kind of physical phenomena producing it. In this paper, we present a modular system composed by three blocs: 1) Representation, 2) Dimensionality reduction and 3) Classification. The originality of our work consists in the use of a new wavelet called "Ben wavelet" in the representation stage. For the dimensionality reduction, we propose a new algorithm based on the random projection and the principal component analysis.
", keywords = "Seismic signals, Ben Wavelet, Dimensionality
reduction, Artificial neural networks, Classification.", volume = "2", number = "7", pages = "2552-5", }
