Abstract
Adaptive subband structures have been proposed with the objective of increasing the convergence speed and/or reducing the computational complexity of adaptation algorithms for applications which require a large number of adaptive coefficients. In this paper we propose an online blind source separation method for convolutive mixtures which employs a real-coefficient uniform subband structure with critical sampling and extra filters that cancel aliasing between adjacent channels. Since the separation filters in the subbands work at reduced sampling rates, the proposed method presents smaller computational complexity and larger steady-state signal to noise interference ratio when compared to the corresponding fullband algorithm.
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© 2009 Springer-Verlag Berlin Heidelberg
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Batalheiro, P.B., Petraglia, M.R., Haddad, D.B. (2009). Online Subband Blind Source Separation for Convolutive Mixtures Using a Uniform Filter Bank with Critical Sampling. In: Adali, T., Jutten, C., Romano, J.M.T., Barros, A.K. (eds) Independent Component Analysis and Signal Separation. ICA 2009. Lecture Notes in Computer Science, vol 5441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00599-2_27
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DOI: https://doi.org/10.1007/978-3-642-00599-2_27
Publisher Name: Springer, Berlin, Heidelberg
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