Abstract
In this paper, a two-layer neural network is presented that organizes itself to perform blind source separation, i.e. it extracts the unknown independent source signals out of their linear mixtures. The convergence behaviour of the network is analyzed, and experimental results of separating historical speeches of four different speakers are presented.
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© 1998 Springer-Verlag Wien
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Freisleben, B., Hagen, C., Borschbach, M. (1998). Blind Source Separation via Unsupervised Learning. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_25
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DOI: https://doi.org/10.1007/978-3-7091-6492-1_25
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
eBook Packages: Springer Book Archive