Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals

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Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals

Factor analysis is broadly used as a powerful unsupervised machine learning tool for reconstruction of hidden features in recorded mixtures of signals. In the case of a linear approximation, the mixtures can be decomposed by a variety of model-free Blind Source Separation (BSS) algorithms. Most of the available BSS algorithms consider an instantaneous mixing of signals, while the case when the ...

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ژورنال

عنوان ژورنال: PLOS ONE

سال: 2018

ISSN: 1932-6203

DOI: 10.1371/journal.pone.0193974