Blind Source Separation via Unsupervised Learning
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.
KeywordsLearning Rule Independent Component Analysis Convergence Behaviour Blind Source Separation Linear Mixture
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