Abstract
In one form or another, noise is present in almost any signal processing problem. Moreover, its profile is very often essentially unknown. Efficient automatic methods for its detection and filtering are thus very useful, and more so if they only rely on the signal internal structure. In this note we will shown how one such method can be obtained through the analysis of the Karhunen-Loève transformation of a certain signal window, and its realization by a linear autoassociative network.
With partial support from TIC-CICyT, grant 95-965.
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© 1997 Springer-Verlag Berlin Heidelberg
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Cruz, C.S., Dorronsoro, J.R., Sigüenza, J.A., López, V. (1997). Noise discrimination and autoassociative neural networks. In: Mira, J., Moreno-Díaz, R., Cabestany, J. (eds) Biological and Artificial Computation: From Neuroscience to Technology. IWANN 1997. Lecture Notes in Computer Science, vol 1240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032581
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DOI: https://doi.org/10.1007/BFb0032581
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