Abstract
In this paper, we present several neural networks approaches to deal with the problem of source localisation in signal processing. To locate a far field source, we have to retrieve the propagation delays between sensors. They are the only relevant temporal information we can handle on the signal.
As we have to process temporal data, we study different time representations in neural networks. First, time may be represented explicitly and externally with multilayer perceptrons. Then, we use a time-delay neural network that appears to be more suitable to represent explicitly the temporal nature of the signal. Last, we study recurrent neural networks that represent time in their internal organisation.
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© 1995 Springer-Verlag/Wien
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Colnet, B., Durand, S. (1995). Application of Temporal Neural Networks to Source Localisation. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-7535-4_118
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DOI: https://doi.org/10.1007/978-3-7091-7535-4_118
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82692-8
Online ISBN: 978-3-7091-7535-4
eBook Packages: Springer Book Archive