Abstract
Sparse Blind Source Separation (BSS) problems have recently received some attention. And some of them have been proposed for the unknown number of sources. However, they only consider the overdetermined case (i.e. with more sources than sensors). In the practical BSS, there are not prior assumptions on the number of sources. In this paper, we use cluster and Principal Component Analysis (PCA) to estimate the number of the sources and the separation matrix, and then make the estimation of sources. Experiments with speech signals demonstrate the validity of the proposed method.
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Zhang, Y., Li, H., Qi, R. (2010). Sparse Source Separation with Unknown Source Number. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13498-2_22
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DOI: https://doi.org/10.1007/978-3-642-13498-2_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13497-5
Online ISBN: 978-3-642-13498-2
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