Abstract
In many Signal Processing applications, data sampled by sensors comprise a mixture of signals from different sources. The problem of separation lies in the reconstruction of sources from the mixtures. In this paper a new method is proposed for the separation of sources, based on geometrical considerations. After a brief introduction, we present the principles of the new method and provide a description of the algorithm and map this on an artificial neural network. Finally we give examples with synthetic and real signals to illustrate the efficiency and utility of the network.
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© 1995 Springer-Verlag Berlin Heidelberg
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Puntonet, C.G., Rodríguez-Alvarez, M., Prieto, A. (1995). A geometrical based procedure for source separation mapped to a neural network. In: Mira, J., Sandoval, F. (eds) From Natural to Artificial Neural Computation. IWANN 1995. Lecture Notes in Computer Science, vol 930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59497-3_265
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DOI: https://doi.org/10.1007/3-540-59497-3_265
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